{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SoRB.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YJhveYqWn-tA"
      },
      "source": [
        "##### Copyright 2019 Google LLC.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "pKe5zvMRoAB7",
        "colab": {}
      },
      "source": [
        "#@title License\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Dc1snEt7qwn6"
      },
      "source": [
        "# [_Search on the Replay Buffer_: Bridging Planning and Reinforcement Learning](https://arxiv.org/abs/1906.05253)\n",
        "\n",
        "*Benjamin Eysenbach, Ruslan Salakhutdinov, and Sergey Levine*\n",
        "\n",
        "\n",
        "What is *SoRB*? *SoRB* is a machine learning algorithm that learns to make decisions to reach goals. Typically, these sorts of control algorithms either rely on planning methods or reinforcement learning methods, both of which have limitations. Planning algorithms can reason over long horizons, but cannot deal with high-dimensional observations. Reinforcement learning algorithms fail to plan over long distances. *SoRB* attempts to combine the best of both algorithms.\n",
        "\n",
        "\n",
        "<center><img src=\"http://drive.google.com/uc?export=view&id=1LCqQJb06vyBOHl1uX-bi1p_LvuDMfPIF\" \n",
        "alt=\"search on the replay buffer\" width=\"800px\"/></center>\n",
        "The figure above highlights the basic idea.\n",
        "\n",
        "* (a) Goal-conditioned RL often fails to reach distant goals, but can successfully reach nearby goals (indicated in green).\n",
        "* (b) Our goal is to use observations in our replay buffer (yellow squares) as waypoints leading to the goal.\n",
        "* (c) We automatically find these waypoints by using the agent's value function to predict when two states are nearby, and building the corresponding graph.\n",
        "* (d) We run graph search to find the sequence of waypoints (blue arrows), and then use our goal-conditioned policy to reach each waypoint.\n",
        "\n",
        "<center><img src=\"http://drive.google.com/uc?export=view&id=1RsSVVYEcRADJmd78JYLARR0nGJtrpIN9\" \n",
        "alt=\"search on the replay buffer\" width=\"400px\"/></center>\n",
        "In our paper, we show how this algorithm can be used to solve complex visual navigation tasks, like the one shown above. In this colab notebook, we implement a basic version of *SoRB* on a simple navigation task. Interactive visualizations below allow you to explore the effect of various hyperparameters, as well as train your own agents.\n",
        "\n",
        "### Related Work\n",
        "A number of prior works have proposed methods for learning goal-conditioned policies and combining planning with RL. We encourage you to check out these related works:\n",
        "* Kaelbling, Leslie Pack. \"Learning to achieve goals.\" IJCAI. 1993.\n",
        "* Schaul, Tom, et al. \"Universal value function approximators.\" International conference on machine learning. 2015.\n",
        "* Pong, Vitchyr, et al. \"Temporal difference models: Model-free deep rl for model-based control.\" arXiv preprint arXiv:1802.09081 (2018).\n",
        "* Francis, Anthony, et al. \"Long-Range Indoor Navigation with PRM-RL.\" arXiv preprint arXiv:1902.09458 (2019).\n",
        "* Savinov, Nikolay, Alexey Dosovitskiy, and Vladlen Koltun. \"Semi-parametric topological memory for navigation.\" arXiv preprint arXiv:1803.00653 (2018).\n",
        "\n",
        "\n",
        "If you find this code useful, please consider citing our paper: [https://arxiv.org/abs/1906.05253](https://arxiv.org/abs/1906.05253)\n",
        "```\n",
        "@incollection{eysenbach2019,\n",
        "  title = {Search on the Replay Buffer: Bridging Planning and Reinforcement Learning},\n",
        "  author = {Eysenbach, Benjamin and Salakhutdinov, Russ R and Levine, Sergey},\n",
        "  booktitle = {Advances in Neural Information Processing Systems 32},\n",
        "  editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\\textquotesingle Alch\\'{e}-Buc and E. Fox and R. Garnett},\n",
        "  pages = {15246--15257},\n",
        "  year = {2019},\n",
        "  publisher = {Curran Associates, Inc.},\n",
        "  url = {http://papers.nips.cc/paper/9660-search-on-the-replay-buffer-bridging-planning-and-reinforcement-learning.pdf}\n",
        "}\n",
        "```\n",
        "\n",
        "### Getting Started\n",
        "To get started, click the \"Connect\" button in the top right corner of the screen. You should see a green checkmark next to some stats about RAM and Disk. Run each of the cells below, either by clicking the \"run cell\" button on the left of each cell, or by pressing [ctrl]+[enter]. **Double click on any cell to see the code.**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWsm1CJgumDO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install tf-agents==0.4.0\n",
        "!pip install tensorflow-probability==0.9.0\n",
        "!pip install tensorflow==2.1.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "bbiO8OnH8Bk7",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import collections\n",
        "import random\n",
        "import time\n",
        "import tqdm\n",
        "\n",
        "import gym\n",
        "import gym.spaces\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import scipy.sparse.csgraph\n",
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "\n",
        "from tf_agents.agents import tf_agent\n",
        "from tf_agents.agents.ddpg import actor_network\n",
        "from tf_agents.agents.ddpg import critic_network\n",
        "from tf_agents.drivers import dynamic_step_driver\n",
        "from tf_agents.environments import gym_wrapper\n",
        "from tf_agents.environments import tf_py_environment\n",
        "from tf_agents.environments import wrappers\n",
        "from tf_agents.eval import metric_utils\n",
        "from tf_agents.metrics import tf_metrics\n",
        "from tf_agents.networks import utils\n",
        "from tf_agents.policies import actor_policy\n",
        "from tf_agents.policies import ou_noise_policy\n",
        "from tf_agents.policies import tf_policy\n",
        "from tf_agents.replay_buffers import tf_uniform_replay_buffer\n",
        "from tf_agents.trajectories import time_step\n",
        "from tf_agents.trajectories import trajectory\n",
        "from tf_agents.utils import common"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "esd_jQgISaff",
        "outputId": "81e9b6ce-cc96-43fd-fcec-e70f13ee638c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "source": [
        "#@title Implement the 2D navigation environment and helper functions.\n",
        "WALLS = {\n",
        "    'Small':\n",
        "        np.array([[0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0]]),\n",
        "    'Cross':\n",
        "        np.array([[0, 0, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 1, 0, 0, 0],\n",
        "                  [0, 0, 0, 1, 0, 0, 0],\n",
        "                  [0, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 0, 0, 1, 0, 0, 0],\n",
        "                  [0, 0, 0, 1, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 0, 0]]),\n",
        "    'FourRooms':\n",
        "        np.array([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
        "                  [1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1],\n",
        "                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]]),\n",
        "    'Spiral5x5':\n",
        "        np.array([[0, 0, 0, 0, 0],\n",
        "                  [0, 1, 1, 1, 1],\n",
        "                  [0, 1, 0, 0, 1],\n",
        "                  [0, 1, 1, 0, 1],\n",
        "                  [0, 0, 0, 0, 1]]),\n",
        "    'Spiral7x7':\n",
        "        np.array([[1, 1, 1, 1, 1, 1, 1],\n",
        "                  [1, 0, 0, 0, 0, 0, 0],\n",
        "                  [1, 0, 1, 1, 1, 1, 0],\n",
        "                  [1, 0, 1, 0, 0, 1, 0],\n",
        "                  [1, 0, 1, 1, 0, 1, 0],\n",
        "                  [1, 0, 0, 0, 0, 1, 0],\n",
        "                  [1, 1, 1, 1, 1, 1, 0]]),\n",
        "    'Spiral9x9':\n",
        "        np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "                  [0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "                  [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
        "                  [0, 1, 0, 1, 1, 1, 1, 0, 1],\n",
        "                  [0, 1, 0, 1, 0, 0, 1, 0, 1],\n",
        "                  [0, 1, 0, 1, 1, 0, 1, 0, 1],\n",
        "                  [0, 1, 0, 0, 0, 0, 1, 0, 1],\n",
        "                  [0, 1, 1, 1, 1, 1, 1, 0, 1],\n",
        "                  [0, 0, 0, 0, 0, 0, 0, 0, 1]]),\n",
        "    'Spiral11x11':\n",
        "        np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "                  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "                  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "                  [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "                  [1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0],\n",
        "                  [1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0],\n",
        "                  [1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0],\n",
        "                  [1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0],\n",
        "                  [1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n",
        "                  [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]),\n",
        "    'Maze3x3':\n",
        "        np.array([[0, 0, 0],\n",
        "                  [1, 1, 0],\n",
        "                  [0, 0, 0]]),\n",
        "    'Maze6x6':\n",
        "        np.array([[0, 0, 1, 0, 0, 0],\n",
        "                  [1, 0, 1, 0, 1, 0],\n",
        "                  [0, 0, 1, 0, 1, 1],\n",
        "                  [0, 1, 1, 0, 0, 1],\n",
        "                  [0, 0, 1, 1, 0, 1],\n",
        "                  [1, 0, 0, 0, 0, 1]]),\n",
        "    'Maze11x11':\n",
        "        np.array([[0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n",
        "                  [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "                  [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "                  [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "                  [0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n",
        "                  [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n",
        "                  [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0],\n",
        "                  [1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0],\n",
        "                  [0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0],\n",
        "                  [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),\n",
        "    'Tunnel':\n",
        "        np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n",
        "                  [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0],\n",
        "                  [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n",
        "                  [0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "                  [0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n",
        "                  [0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n",
        "                  [0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n",
        "                  [0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0],\n",
        "                  [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0],\n",
        "                  [0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n",
        "                  [0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "                  [0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "                  [0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "                  [0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0],\n",
        "                  [0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0],\n",
        "                  [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]]),\n",
        "    'U':\n",
        "        np.array([[0, 0, 0],\n",
        "                  [0, 1, 0],\n",
        "                  [0, 1, 0],\n",
        "                  [0, 1, 0],\n",
        "                  [1, 1, 0],\n",
        "                  [0, 1, 0],\n",
        "                  [0, 1, 0],\n",
        "                  [0, 1, 0],\n",
        "                  [0, 0, 0]]),\n",
        "    'Tree':\n",
        "        np.array([\n",
        "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "            [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1],\n",
        "            [1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1],\n",
        "            [1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1],\n",
        "            [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n",
        "            [0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n",
        "            [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "        ]),\n",
        "    'UMulti':\n",
        "        np.array([\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "         ]),\n",
        "    'FlyTrapSmall':\n",
        "        np.array([\n",
        "            [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n",
        "            [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n",
        "            [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
        "            [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n",
        "            [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n",
        "         ]),\n",
        "    'FlyTrapBig':\n",
        "        np.array([\n",
        "            [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n",
        "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "            [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n",
        "         ]),\n",
        "    'Galton':\n",
        "        np.array([\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n",
        "            [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "            [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
        "        ]),\n",
        "}\n",
        "\n",
        "def resize_walls(walls, factor):\n",
        "  \"\"\"Increase the environment by rescaling.\n",
        "  \n",
        "  Args:\n",
        "    walls: 0/1 array indicating obstacle locations.\n",
        "    factor: (int) factor by which to rescale the environment.\"\"\"\n",
        "  (height, width) = walls.shape\n",
        "  row_indices = np.array([i for i in range(height) for _ in range(factor)])\n",
        "  col_indices = np.array([i for i in range(width) for _ in range(factor)])\n",
        "  walls = walls[row_indices]\n",
        "  walls = walls[:, col_indices]\n",
        "  assert walls.shape == (factor * height, factor * width)\n",
        "  return walls\n",
        "\n",
        "\n",
        "\n",
        "class PointEnv(gym.Env):\n",
        "  \"\"\"Abstract class for 2D navigation environments.\"\"\"\n",
        "\n",
        "  def __init__(self, walls=None, resize_factor=1,\n",
        "               action_noise=1.0):\n",
        "    \"\"\"Initialize the point environment.\n",
        "\n",
        "    Args:\n",
        "      walls: (str) name of one of the maps defined above.\n",
        "      resize_factor: (int) Scale the map by this factor.\n",
        "      action_noise: (float) Standard deviation of noise to add to actions. Use 0\n",
        "        to add no noise.\n",
        "    \"\"\"\n",
        "    if resize_factor > 1:\n",
        "      self._walls = resize_walls(WALLS[walls], resize_factor)\n",
        "    else:\n",
        "      self._walls = WALLS[walls]\n",
        "    self._apsp = self._compute_apsp(self._walls)\n",
        "    (height, width) = self._walls.shape\n",
        "    self._height = height\n",
        "    self._width = width\n",
        "    self._action_noise = action_noise\n",
        "    self.action_space = gym.spaces.Box(\n",
        "        low=np.array([-1.0, -1.0]),\n",
        "        high=np.array([1.0, 1.0]),\n",
        "        dtype=np.float32)\n",
        "    self.observation_space = gym.spaces.Box(\n",
        "        low=np.array([0.0, 0.0]),\n",
        "        high=np.array([self._height, self._width]),\n",
        "        dtype=np.float32)\n",
        "    self.reset()\n",
        "\n",
        "  def _sample_empty_state(self):\n",
        "    candidate_states = np.where(self._walls == 0)\n",
        "    num_candidate_states = len(candidate_states[0])\n",
        "    state_index = np.random.choice(num_candidate_states)\n",
        "    state = np.array([candidate_states[0][state_index],\n",
        "                      candidate_states[1][state_index]],\n",
        "                     dtype=np.float)\n",
        "    state += np.random.uniform(size=2)\n",
        "    assert not self._is_blocked(state)\n",
        "    return state\n",
        "      \n",
        "  def reset(self):\n",
        "    self.state = self._sample_empty_state()\n",
        "    return self.state.copy()\n",
        "  \n",
        "  def _get_distance(self, obs, goal):\n",
        "    \"\"\"Compute the shortest path distance.\n",
        "    \n",
        "    Note: This distance is *not* used for training.\"\"\"\n",
        "    (i1, j1) = self._discretize_state(obs)\n",
        "    (i2, j2) = self._discretize_state(goal)\n",
        "    return self._apsp[i1, j1, i2, j2]\n",
        "\n",
        "  def _discretize_state(self, state, resolution=1.0):\n",
        "    (i, j) = np.floor(resolution * state).astype(np.int)\n",
        "    # Round down to the nearest cell if at the boundary.\n",
        "    if i == self._height:\n",
        "      i -= 1\n",
        "    if j == self._width:\n",
        "      j -= 1\n",
        "    return (i, j)\n",
        "  \n",
        "  def _is_blocked(self, state):\n",
        "    if not self.observation_space.contains(state):\n",
        "      return True\n",
        "    (i, j) = self._discretize_state(state)\n",
        "    return (self._walls[i, j] == 1)\n",
        "\n",
        "  def step(self, action):\n",
        "    if self._action_noise > 0:\n",
        "      action += np.random.normal(0, self._action_noise)\n",
        "    action = np.clip(action, self.action_space.low, self.action_space.high)\n",
        "    assert self.action_space.contains(action)\n",
        "    num_substeps = 10\n",
        "    dt = 1.0 / num_substeps\n",
        "    num_axis = len(action)\n",
        "    for _ in np.linspace(0, 1, num_substeps):\n",
        "      for axis in range(num_axis):\n",
        "        new_state = self.state.copy()\n",
        "        new_state[axis] += dt * action[axis]\n",
        "        if not self._is_blocked(new_state):\n",
        "          self.state = new_state\n",
        "\n",
        "    done = False\n",
        "    rew = -1.0 * np.linalg.norm(self.state)\n",
        "    return self.state.copy(), rew, done, {}\n",
        "\n",
        "  @property\n",
        "  def walls(self):\n",
        "    return self._walls\n",
        "\n",
        "  def _compute_apsp(self, walls):\n",
        "    (height, width) = walls.shape\n",
        "    g = nx.Graph()\n",
        "    # Add all the nodes\n",
        "    for i in range(height):\n",
        "      for j in range(width):\n",
        "        if walls[i, j] == 0:\n",
        "          g.add_node((i, j))\n",
        "\n",
        "    # Add all the edges\n",
        "    for i in range(height):\n",
        "      for j in range(width):\n",
        "        for di in [-1, 0, 1]:\n",
        "          for dj in [-1, 0, 1]:\n",
        "            if di == dj == 0: continue  # Don't add self loops\n",
        "            if i + di < 0 or i + di > height - 1: continue  # No cell here\n",
        "            if j + dj < 0 or j + dj > width - 1: continue  # No cell here\n",
        "            if walls[i, j] == 1: continue  # Don't add edges to walls\n",
        "            if walls[i + di, j + dj] == 1: continue  # Don't add edges to walls\n",
        "            g.add_edge((i, j), (i + di, j + dj))\n",
        "\n",
        "    # dist[i, j, k, l] is path from (i, j) -> (k, l)\n",
        "    dist = np.full((height, width, height, width), np.float('inf'))\n",
        "    for ((i1, j1), dist_dict) in nx.shortest_path_length(g):\n",
        "      for ((i2, j2), d) in dist_dict.items():\n",
        "        dist[i1, j1, i2, j2] = d\n",
        "    return dist\n",
        "\n",
        "class GoalConditionedPointWrapper(gym.Wrapper):\n",
        "  \"\"\"Wrapper that appends goal to state produced by environment.\"\"\"\n",
        "\n",
        "  \n",
        "  def __init__(self, env, prob_constraint=0.8, min_dist=0, max_dist=4,\n",
        "               threshold_distance=1.0):\n",
        "    \"\"\"Initialize the environment.\n",
        "\n",
        "    Args:\n",
        "      env: an environment.\n",
        "      prob_constraint: (float) Probability that the distance constraint is\n",
        "        followed after resetting.\n",
        "      min_dist: (float) When the constraint is enforced, ensure the goal is at\n",
        "        least this far from the initial state.\n",
        "      max_dist: (float) When the constraint is enforced, ensure the goal is at\n",
        "        most this far from the initial state.\n",
        "      threshold_distance: (float) States are considered equivalent if they are\n",
        "        at most this far away from one another.\n",
        "    \"\"\"\n",
        "    self._threshold_distance = threshold_distance\n",
        "    self._prob_constraint = prob_constraint\n",
        "    self._min_dist = min_dist\n",
        "    self._max_dist = max_dist\n",
        "    super(GoalConditionedPointWrapper, self).__init__(env)\n",
        "    self.observation_space = gym.spaces.Dict({\n",
        "        'observation': env.observation_space,\n",
        "        'goal': env.observation_space,\n",
        "    })\n",
        "  \n",
        "  def _normalize_obs(self, obs):\n",
        "    return np.array([\n",
        "        obs[0] / float(self.env._height),\n",
        "        obs[1] / float(self.env._width)\n",
        "    ])\n",
        "\n",
        "  def reset(self):\n",
        "    goal = None\n",
        "    count = 0\n",
        "    while goal is None:\n",
        "      obs = self.env.reset()\n",
        "      (obs, goal) = self._sample_goal(obs)\n",
        "      count += 1\n",
        "      if count > 1000:\n",
        "        print('WARNING: Unable to find goal within constraints.')\n",
        "    self._goal = goal\n",
        "    return {'observation': self._normalize_obs(obs),\n",
        "            'goal': self._normalize_obs(self._goal)}\n",
        "\n",
        "  def step(self, action):\n",
        "    obs, _, _, _ = self.env.step(action)\n",
        "    rew = -1.0\n",
        "    done = self._is_done(obs, self._goal)\n",
        "    return {'observation': self._normalize_obs(obs),\n",
        "            'goal': self._normalize_obs(self._goal)}, rew, done, {}\n",
        "\n",
        "  def set_sample_goal_args(self, prob_constraint=None,\n",
        "                           min_dist=None, max_dist=None):\n",
        "    assert prob_constraint is not None\n",
        "    assert min_dist is not None\n",
        "    assert max_dist is not None\n",
        "    assert min_dist >= 0\n",
        "    assert max_dist >= min_dist\n",
        "    self._prob_constraint = prob_constraint\n",
        "    self._min_dist = min_dist\n",
        "    self._max_dist = max_dist\n",
        "\n",
        "  def _is_done(self, obs, goal):\n",
        "    \"\"\"Determines whether observation equals goal.\"\"\"\n",
        "    return np.linalg.norm(obs - goal) < self._threshold_distance\n",
        "\n",
        "  def _sample_goal(self, obs):\n",
        "    \"\"\"Sampled a goal state.\"\"\"\n",
        "    if np.random.random() < self._prob_constraint:\n",
        "      return self._sample_goal_constrained(obs, self._min_dist, self._max_dist)\n",
        "    else:\n",
        "      return self._sample_goal_unconstrained(obs)\n",
        "\n",
        "  def _sample_goal_constrained(self, obs, min_dist, max_dist):\n",
        "    \"\"\"Samples a goal with dist min_dist <= d(obs, goal) <= max_dist.\n",
        "\n",
        "    Args:\n",
        "      obs: observation (without goal).\n",
        "      min_dist: (int) minimum distance to goal.\n",
        "      max_dist: (int) maximum distance to goal.\n",
        "    Returns:\n",
        "      obs: observation (without goal).\n",
        "      goal: a goal state.\n",
        "    \"\"\"\n",
        "    (i, j) = self.env._discretize_state(obs)\n",
        "    mask = np.logical_and(self.env._apsp[i, j] >= min_dist,\n",
        "                          self.env._apsp[i, j] <= max_dist)\n",
        "    mask = np.logical_and(mask, self.env._walls == 0)\n",
        "    candidate_states = np.where(mask)\n",
        "    num_candidate_states = len(candidate_states[0])\n",
        "    if num_candidate_states == 0:\n",
        "      return (obs, None)\n",
        "    goal_index = np.random.choice(num_candidate_states)\n",
        "    goal = np.array([candidate_states[0][goal_index],\n",
        "                     candidate_states[1][goal_index]],\n",
        "                    dtype=np.float)\n",
        "    goal += np.random.uniform(size=2)\n",
        "    dist_to_goal = self.env._get_distance(obs, goal)\n",
        "    assert min_dist <= dist_to_goal <= max_dist\n",
        "    assert not self.env._is_blocked(goal)\n",
        "    return (obs, goal)\n",
        "    \n",
        "  def _sample_goal_unconstrained(self, obs):\n",
        "    \"\"\"Samples a goal without any constraints.\n",
        "\n",
        "    Args:\n",
        "      obs: observation (without goal).\n",
        "    Returns:\n",
        "      obs: observation (without goal).\n",
        "      goal: a goal state.\n",
        "    \"\"\"\n",
        "    return (obs, self.env._sample_empty_state())\n",
        "    \n",
        "  @property\n",
        "  def max_goal_dist(self):\n",
        "    apsp = self.env._apsp\n",
        "    return np.max(apsp[np.isfinite(apsp)])\n",
        "    \n",
        "\n",
        "class NonTerminatingTimeLimit(wrappers.PyEnvironmentBaseWrapper):\n",
        "  \"\"\"Resets the environment without setting done = True.\n",
        "\n",
        "  Resets the environment if either these conditions holds:\n",
        "    1. The base environment returns done = True\n",
        "    2. The time limit is exceeded.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, env, duration):\n",
        "    super(NonTerminatingTimeLimit, self).__init__(env)\n",
        "    self._duration = duration\n",
        "    self._step_count = None\n",
        "\n",
        "  def _reset(self):\n",
        "    self._step_count = 0\n",
        "    return self._env.reset()\n",
        "\n",
        "  @property\n",
        "  def duration(self):\n",
        "    return self._duration\n",
        "\n",
        "  def _step(self, action):\n",
        "    if self._step_count is None:\n",
        "      return self.reset()\n",
        "\n",
        "    ts = self._env.step(action)\n",
        "\n",
        "    self._step_count += 1\n",
        "    if self._step_count >= self._duration or ts.is_last():\n",
        "      self._step_count = None\n",
        "\n",
        "    return ts\n",
        "  \n",
        "def env_load_fn(environment_name,\n",
        "         max_episode_steps=None,\n",
        "         resize_factor=1,\n",
        "         gym_env_wrappers=(GoalConditionedPointWrapper,),\n",
        "         terminate_on_timeout=False):\n",
        "  \"\"\"Loads the selected environment and wraps it with the specified wrappers.\n",
        "\n",
        "  Args:\n",
        "    environment_name: Name for the environment to load.\n",
        "    max_episode_steps: If None the max_episode_steps will be set to the default\n",
        "      step limit defined in the environment's spec. No limit is applied if set\n",
        "      to 0 or if there is no timestep_limit set in the environment's spec.\n",
        "    gym_env_wrappers: Iterable with references to wrapper classes to use\n",
        "      directly on the gym environment.\n",
        "    terminate_on_timeout: Whether to set done = True when the max episode\n",
        "      steps is reached.\n",
        "\n",
        "  Returns:\n",
        "    A PyEnvironmentBase instance.\n",
        "  \"\"\"\n",
        "  gym_env = PointEnv(walls=environment_name,\n",
        "                     resize_factor=resize_factor)\n",
        "  \n",
        "  for wrapper in gym_env_wrappers:\n",
        "    gym_env = wrapper(gym_env)\n",
        "  env = gym_wrapper.GymWrapper(\n",
        "      gym_env,\n",
        "      discount=1.0,\n",
        "      auto_reset=True,\n",
        "  )\n",
        "\n",
        "  if max_episode_steps > 0:\n",
        "    if terminate_on_timeout:\n",
        "      env = wrappers.TimeLimit(env, max_episode_steps)\n",
        "    else:\n",
        "      env = NonTerminatingTimeLimit(env, max_episode_steps)\n",
        "\n",
        "  return tf_py_environment.TFPyEnvironment(env)\n",
        "\n",
        "def plot_walls(walls):\n",
        "  walls = walls.T\n",
        "  (height, width) = walls.shape\n",
        "  for (i, j) in zip(*np.where(walls)):\n",
        "    x = np.array([j, j+1]) / float(width)\n",
        "    y0 = np.array([i, i]) / float(height)\n",
        "    y1 = np.array([i+1, i+1]) / float(height)\n",
        "    plt.fill_between(x, y0, y1, color='grey')\n",
        "  plt.xlim([0, 1])\n",
        "  plt.ylim([0, 1])\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  \n",
        "plt.figure(figsize=(12, 7))\n",
        "for index, (name, walls) in enumerate(WALLS.items()):\n",
        "  plt.subplot(3, 6, index + 1)\n",
        "  plt.title(name)\n",
        "  plot_walls(walls)\n",
        "plt.subplots_adjust(wspace=0.1, hspace=0.2)\n",
        "plt.suptitle('Navigation Environments', fontsize=20)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 17 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "3nLaKviAXrq9",
        "colab": {}
      },
      "source": [
        "#@title Implement the goal-conditioned actor and critic.\n",
        "\n",
        "def set_goal(traj, goal):\n",
        "  \"\"\"Sets the goal of a Trajectory or TimeStep.\"\"\"\n",
        "  for obs_field in ['observation', 'goal']:\n",
        "    assert obs_field in traj.observation.keys()\n",
        "  obs = traj.observation['observation']\n",
        "  tf.nest.assert_same_structure(obs, goal)\n",
        "  modified_traj = traj._replace(\n",
        "      observation={'observation': obs, 'goal': goal})\n",
        "  return modified_traj\n",
        "\n",
        "def merge_obs_goal(observations):\n",
        "  \"\"\"Merge the observation and goal fields into a single tensor.\n",
        "\n",
        "  If both are 1D, we concatenate the observation and goal together. If both are\n",
        "  3D, we stack along the third axis, so the resulting tensor has\n",
        "  shape (H x W x 2 * D).\n",
        "\n",
        "  Args:\n",
        "    observations: Dictionary-type observations.\n",
        "  Returns:\n",
        "    a merged observation\n",
        "  \"\"\"\n",
        "  obs = observations['observation']\n",
        "  goal = observations['goal']\n",
        "\n",
        "  assert obs.shape == goal.shape\n",
        "  # For 1D observations, simply concatenate them together.\n",
        "  assert len(obs.shape) == 2\n",
        "  modified_observations = tf.concat([obs, goal], axis=-1)\n",
        "  assert obs.shape[0] == modified_observations.shape[0]\n",
        "  assert modified_observations.shape[1] == obs.shape[1] + goal.shape[1]\n",
        "  return modified_observations\n",
        "\n",
        "\n",
        "class GoalConditionedActorNetwork(actor_network.ActorNetwork):\n",
        "  \"\"\"Actor network that takes observations and goals as inputs.\"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               input_tensor_spec,\n",
        "               output_tensor_spec,\n",
        "               **kwargs):\n",
        "    modified_tensor_spec = None\n",
        "    super(GoalConditionedActorNetwork, self).__init__(\n",
        "        modified_tensor_spec, output_tensor_spec,\n",
        "        fc_layer_params=(256, 256),\n",
        "        **kwargs)\n",
        "    self._input_tensor_spec = input_tensor_spec\n",
        "\n",
        "  def call(self, observations, step_type=(), network_state=()):\n",
        "    modified_observations = merge_obs_goal(observations)\n",
        "    return super(GoalConditionedActorNetwork, self).call(\n",
        "        modified_observations, step_type=step_type, network_state=network_state)\n",
        "\n",
        "\n",
        "class GoalConditionedCriticNetwork(critic_network.CriticNetwork):\n",
        "  \"\"\"Actor network that takes observations and goals as inputs.\n",
        "\n",
        "  Further modified so it can make multiple predictions.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               input_tensor_spec,\n",
        "               observation_conv_layer_params=None,\n",
        "               observation_fc_layer_params=(256,),\n",
        "               action_fc_layer_params=None,\n",
        "               joint_fc_layer_params=(256,),\n",
        "               activation_fn=tf.nn.relu,\n",
        "               name='CriticNetwork',\n",
        "               output_dim=None):\n",
        "    \"\"\"Creates an instance of `CriticNetwork`.\n",
        "\n",
        "    Args:\n",
        "      input_tensor_spec: A tuple of (observation, action) each a nest of\n",
        "        `tensor_spec.TensorSpec` representing the inputs.\n",
        "      observation_conv_layer_params: Optional list of convolution layer\n",
        "        parameters for observations, where each item is a length-three tuple\n",
        "        indicating (num_units, kernel_size, stride).\n",
        "      observation_fc_layer_params: Optional list of fully connected parameters\n",
        "        for observations, where each item is the number of units in the layer.\n",
        "      action_fc_layer_params: Optional list of fully connected parameters for\n",
        "        actions, where each item is the number of units in the layer.\n",
        "      joint_fc_layer_params: Optional list of fully connected parameters after\n",
        "        merging observations and actions, where each item is the number of units\n",
        "        in the layer.\n",
        "      activation_fn: Activation function, e.g. tf.nn.relu, slim.leaky_relu, ...\n",
        "      name: A string representing name of the network.\n",
        "      output_dim: An integer specifying the number of outputs. If None, output\n",
        "        will be flattened.\n",
        "\n",
        "    \"\"\"\n",
        "    self._output_dim = output_dim\n",
        "    (_, action_spec) = input_tensor_spec\n",
        "    modified_obs_spec = None\n",
        "    modified_tensor_spec = (modified_obs_spec, action_spec)\n",
        "\n",
        "    super(critic_network.CriticNetwork, self).__init__(\n",
        "        input_tensor_spec=modified_tensor_spec,\n",
        "        state_spec=(),\n",
        "        name=name)\n",
        "    self._input_tensor_spec = input_tensor_spec\n",
        "\n",
        "    flat_action_spec = tf.nest.flatten(action_spec)\n",
        "    if len(flat_action_spec) > 1:\n",
        "      raise ValueError('Only a single action is supported by this network')\n",
        "    self._single_action_spec = flat_action_spec[0]\n",
        "\n",
        "    self._observation_layers = utils.mlp_layers(\n",
        "        observation_conv_layer_params,\n",
        "        observation_fc_layer_params,\n",
        "        activation_fn=activation_fn,\n",
        "        kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(\n",
        "            scale=1. / 3., mode='fan_in', distribution='uniform'),\n",
        "        name='observation_encoding')\n",
        "\n",
        "    self._action_layers = utils.mlp_layers(\n",
        "        None,\n",
        "        action_fc_layer_params,\n",
        "        activation_fn=activation_fn,\n",
        "        kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(\n",
        "            scale=1. / 3., mode='fan_in', distribution='uniform'),\n",
        "        name='action_encoding')\n",
        "\n",
        "    self._joint_layers = utils.mlp_layers(\n",
        "        None,\n",
        "        joint_fc_layer_params,\n",
        "        activation_fn=activation_fn,\n",
        "        kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(\n",
        "            scale=1. / 3., mode='fan_in', distribution='uniform'),\n",
        "        name='joint_mlp')\n",
        "\n",
        "    self._joint_layers.append(\n",
        "        tf.keras.layers.Dense(\n",
        "            self._output_dim if self._output_dim is not None else 1,\n",
        "            activation=None,\n",
        "            kernel_initializer=tf.compat.v1.keras.initializers.RandomUniform(\n",
        "                minval=-0.003, maxval=0.003),\n",
        "            name='value'))\n",
        "\n",
        "  def call(self, inputs, step_type=(), network_state=()):\n",
        "    observations, actions = inputs\n",
        "    modified_observations = merge_obs_goal(observations)\n",
        "    modified_inputs = (modified_observations, actions)\n",
        "    output = super(GoalConditionedCriticNetwork, self).call(\n",
        "        modified_inputs, step_type=step_type, network_state=network_state)\n",
        "    (predictions, network_state) = output\n",
        "\n",
        "    # We have to reshape the output, which is flattened by default\n",
        "    if self._output_dim is not None:\n",
        "      predictions = tf.reshape(predictions, [-1, self._output_dim])\n",
        "\n",
        "    return predictions, network_state"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "66Rh9rum8qNh",
        "colab": {}
      },
      "source": [
        "#@title Implement the goal-conditioned agent.\n",
        "\n",
        "class UvfAgent(tf_agent.TFAgent):\n",
        "  \"\"\"A UVF Agent.\"\"\"\n",
        "\n",
        "  def __init__(\n",
        "      self,\n",
        "      time_step_spec,\n",
        "      action_spec,\n",
        "      ou_stddev=1.0,\n",
        "      ou_damping=1.0,\n",
        "      target_update_tau=0.05,\n",
        "      target_update_period=5,\n",
        "      max_episode_steps=None,\n",
        "      ensemble_size=3,\n",
        "      combine_ensemble_method='min',\n",
        "      use_distributional_rl=True):\n",
        "    \"\"\"Creates a Uvf Agent.\n",
        "\n",
        "    Args:\n",
        "      time_step_spec: A `TimeStep` spec of the expected time_steps.\n",
        "      action_spec: A nest of BoundedTensorSpec representing the actions.\n",
        "      ou_stddev: Standard deviation for the Ornstein-Uhlenbeck (OU) noise added\n",
        "        in the default collect policy.\n",
        "      ou_damping: Damping factor for the OU noise added in the default collect\n",
        "        policy.\n",
        "      target_update_tau: Factor for soft update of the target networks.\n",
        "      target_update_period: Period for soft update of the target networks.\n",
        "      max_episode_steps: Int indicating number of steps in an episode. Used for\n",
        "        determining the number of bins for distributional RL.\n",
        "      ensemble_size: (int) Number of models in ensemble of critics.\n",
        "      combine_ensemble_method: (str) At test time, how to combine the distances\n",
        "        predicted by each member of the ensemble. Options are 'mean', 'min',\n",
        "        and 'td3'. The 'td3' option is pessimistic w.r.t. the pdf, and then\n",
        "        takes computes the corresponding distance. The 'min' option takes the\n",
        "        minimum q values, corresponding to taking the maximum predicted\n",
        "        distance. Note that we never aggregate predictions during training.\n",
        "      use_distributional_rl: (bool) Whether to use distributional RL.\n",
        "    \"\"\"\n",
        "    tf.Module.__init__(self, name='UvfAgent')\n",
        "\n",
        "    assert max_episode_steps is not None\n",
        "    self._max_episode_steps = max_episode_steps\n",
        "    self._ensemble_size = ensemble_size\n",
        "    self._use_distributional_rl = use_distributional_rl\n",
        "\n",
        "    # Create the actor\n",
        "    self._actor_network = GoalConditionedActorNetwork(\n",
        "        time_step_spec.observation, action_spec)\n",
        "    self._target_actor_network = self._actor_network.copy(\n",
        "        name='TargetActorNetwork')\n",
        "\n",
        "    # Create a prototypical critic, which we will copy to create the ensemble.\n",
        "    critic_net_input_specs = (time_step_spec.observation, action_spec)\n",
        "    critic_network = GoalConditionedCriticNetwork(\n",
        "        critic_net_input_specs,\n",
        "        output_dim=max_episode_steps if use_distributional_rl else None,\n",
        "    )\n",
        "    self._critic_network_list = []\n",
        "    self._target_critic_network_list = []\n",
        "    for ensemble_index in range(self._ensemble_size):\n",
        "      self._critic_network_list.append(\n",
        "          critic_network.copy(name='CriticNetwork%d' % ensemble_index))\n",
        "      self._target_critic_network_list.append(\n",
        "          critic_network.copy(name='TargetCriticNetwork%d' % ensemble_index))\n",
        "\n",
        "    # Create variables for each net.\n",
        "    net_list = [\n",
        "      self._actor_network, self._target_actor_network\n",
        "    ] + self._critic_network_list + self._target_critic_network_list\n",
        "    for net in net_list:\n",
        "      net.create_variables()\n",
        "\n",
        "    self._actor_optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=3e-4)\n",
        "    self._critic_optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=3e-4)\n",
        "    \n",
        "    self._ou_stddev = ou_stddev\n",
        "    self._ou_damping = ou_damping\n",
        "    self._target_update_tau = target_update_tau\n",
        "    self._target_update_period = target_update_period\n",
        "    \n",
        "    self._update_target = self._get_target_updater(\n",
        "        target_update_tau, target_update_period)\n",
        "\n",
        "    policy = actor_policy.ActorPolicy(\n",
        "        time_step_spec=time_step_spec, action_spec=action_spec,\n",
        "        actor_network=self._actor_network, clip=True)\n",
        "    collect_policy = actor_policy.ActorPolicy(\n",
        "        time_step_spec=time_step_spec, action_spec=action_spec,\n",
        "        actor_network=self._actor_network, clip=False)\n",
        "    collect_policy = ou_noise_policy.OUNoisePolicy(\n",
        "        collect_policy,\n",
        "        ou_stddev=self._ou_stddev,\n",
        "        ou_damping=self._ou_damping,\n",
        "        clip=True)\n",
        "\n",
        "    super(UvfAgent, self).__init__(\n",
        "        time_step_spec,\n",
        "        action_spec,\n",
        "        policy,\n",
        "        collect_policy,\n",
        "        train_sequence_length=2)\n",
        "\n",
        "  def initialize_search(self, active_set, max_search_steps=3,\n",
        "                        combine_ensemble_method='min'):\n",
        "    self._combine_ensemble_method = combine_ensemble_method\n",
        "    self._max_search_steps = max_search_steps\n",
        "    self._active_set_tensor = tf.convert_to_tensor(value=active_set)\n",
        "    pdist = self._get_pairwise_dist(self._active_set_tensor, masked=True,\n",
        "                                    aggregate=combine_ensemble_method)    \n",
        "    distances = scipy.sparse.csgraph.floyd_warshall(pdist, directed=True)\n",
        "    self._distances_tensor = tf.convert_to_tensor(value=distances, dtype=tf.float32)\n",
        "\n",
        "  def _get_pairwise_dist(self, obs_tensor, goal_tensor=None, masked=False,\n",
        "                         aggregate='mean'):\n",
        "    \"\"\"Estimates the pairwise distances.\n",
        "    \n",
        "    Args:\n",
        "      obs_tensor: Tensor containing observations\n",
        "      goal_tensor: (optional) Tensor containing a second set of observations. If\n",
        "        not specified, computes the pairwise distances between obs_tensor and\n",
        "        itself.\n",
        "      masked: (bool) Whether to ignore edges that are too long, as defined by\n",
        "        max_search_steps.\n",
        "      aggregate: (str) How to combine the predictions from the ensemble. Options\n",
        "        are to take the minimum predicted q value (i.e., the maximum distance),\n",
        "        the mean, or to simply return all the predictions.\n",
        "    \"\"\"\n",
        "    if goal_tensor is None:\n",
        "      goal_tensor = obs_tensor\n",
        "    dist_matrix = []\n",
        "    for obs_index in range(obs_tensor.shape[0]):\n",
        "      obs = obs_tensor[obs_index]\n",
        "      obs_repeat_tensor = tf.ones_like(goal_tensor) * tf.expand_dims(obs, 0)\n",
        "      obs_goal_tensor = {'observation': obs_repeat_tensor,\n",
        "                         'goal': goal_tensor}\n",
        "      pseudo_next_time_steps = time_step.transition(obs_goal_tensor,\n",
        "                                                    reward=0.0,  # Ignored\n",
        "                                                    discount=1.0)\n",
        "      dist = self._get_dist_to_goal(pseudo_next_time_steps, aggregate=aggregate)\n",
        "      dist_matrix.append(dist)\n",
        "\n",
        "    pairwise_dist = tf.stack(dist_matrix)\n",
        "    if aggregate is None:\n",
        "      pairwise_dist = tf.transpose(a=pairwise_dist, perm=[1, 0, 2])\n",
        "\n",
        "    if masked:\n",
        "      mask = (pairwise_dist > self._max_search_steps)\n",
        "      return tf.compat.v1.where(mask, tf.fill(pairwise_dist.shape, np.inf), \n",
        "                        pairwise_dist)\n",
        "    else:\n",
        "      return pairwise_dist\n",
        "\n",
        "  def _get_critic_output(self, critic_net_list, next_time_steps,\n",
        "                         actions=None):\n",
        "    \"\"\"Calls the critic net.\n",
        "\n",
        "    Args:\n",
        "      critic_net_list: (list) List of critic networks.\n",
        "      next_time_steps: time_steps holding the observations and step types\n",
        "      actions: (optional) actions to compute the Q values for. If None, returns\n",
        "      the Q values for the best action.\n",
        "    Returns:\n",
        "      q_values_list: (list) List containing a tensor of q values for each member\n",
        "      of the ensemble. For distributional RL, computes the expectation over the\n",
        "      distribution.\n",
        "    \"\"\"\n",
        "    q_values_list = []\n",
        "    critic_net_input = (next_time_steps.observation, actions)\n",
        "    for critic_index in range(self._ensemble_size):\n",
        "      critic_net = critic_net_list[critic_index]\n",
        "      q_values, _ = critic_net(critic_net_input, next_time_steps.step_type)\n",
        "      q_values_list.append(q_values)\n",
        "    return q_values_list\n",
        "  \n",
        "  def _get_expected_q_values(self, next_time_steps, actions=None):\n",
        "    if actions is None:\n",
        "      actions, _ = self._actor_network(next_time_steps.observation,\n",
        "                                        next_time_steps.step_type)\n",
        "\n",
        "    q_values_list = self._get_critic_output(self._critic_network_list,\n",
        "                                            next_time_steps, actions)\n",
        "\n",
        "    expected_q_values_list = []\n",
        "    for q_values in q_values_list:\n",
        "      if self._use_distributional_rl:\n",
        "        q_probs = tf.nn.softmax(q_values, axis=1)\n",
        "        batch_size = q_probs.shape[0]\n",
        "        bin_range = tf.range(1, self._max_episode_steps + 1, dtype=tf.float32)\n",
        "        ### NOTE: We want to compute the value of each bin, which is the\n",
        "        # negative distance. Without properly negating this, the actor is\n",
        "        # optimized to take the *worst* actions.\n",
        "        neg_bin_range = -1.0 * bin_range\n",
        "        tiled_bin_range = tf.tile(tf.expand_dims(neg_bin_range, 0),\n",
        "                                  [batch_size, 1])\n",
        "        assert q_probs.shape == tiled_bin_range.shape\n",
        "\n",
        "        ### Take the inner produce between these two tensors\n",
        "        expected_q_values = tf.reduce_sum(input_tensor=q_probs * tiled_bin_range, axis=1)\n",
        "        expected_q_values_list.append(expected_q_values)\n",
        "      else:\n",
        "        expected_q_values_list.append(q_values)\n",
        "    return tf.stack(expected_q_values_list)\n",
        "\n",
        "  def _get_state_values(self, next_time_steps, actions=None, aggregate='mean'):\n",
        "    \"\"\"Computes the value function, averaging across bins (for distributional RL)\n",
        "    and the ensemble (for bootstrap RL).\n",
        "\n",
        "    Args:\n",
        "      next_time_steps: time_steps holding the observations and step types\n",
        "      actions: actions for which to compute the Q values. If None, uses the\n",
        "      best actions (i.e., returns the value function).\n",
        "    Returns:\n",
        "      state_values: Tensor storing the state values for each sample in the\n",
        "      batch. These values should all be negative.\n",
        "    \"\"\"\n",
        "    with tf.compat.v1.name_scope('state_values'):\n",
        "      expected_q_values = self._get_expected_q_values(next_time_steps, actions)\n",
        "      if aggregate is not None:\n",
        "        if aggregate == 'mean':\n",
        "          expected_q_values = tf.reduce_mean(input_tensor=expected_q_values, axis=0)\n",
        "        elif aggregate == 'min':\n",
        "          expected_q_values = tf.reduce_min(input_tensor=expected_q_values, axis=0)\n",
        "        else:\n",
        "          raise ValueError('Unknown method for combining ensemble: %s' %\n",
        "                           aggregate)\n",
        "\n",
        "      # Clip the q values if not using distributional RL. If using\n",
        "      # distributional RL, the q values are implicitly clipped.\n",
        "      if not self._use_distributional_rl:\n",
        "        min_q_val = -1.0 * self._max_episode_steps\n",
        "        max_q_val = 0.0\n",
        "        expected_q_values = tf.maximum(expected_q_values, min_q_val)\n",
        "        expected_q_values = tf.minimum(expected_q_values, max_q_val)\n",
        "      return expected_q_values\n",
        "\n",
        "  def _get_dist_to_goal(self, next_time_step, aggregate='mean'):\n",
        "    q_values = self._get_state_values(next_time_step, aggregate=aggregate)\n",
        "    return -1.0 * q_values\n",
        "\n",
        "  def _get_waypoint(self, next_time_steps):\n",
        "    obs_tensor = next_time_steps.observation['observation']\n",
        "    goal_tensor = next_time_steps.observation['goal']\n",
        "    obs_to_active_set_dist = self._get_pairwise_dist(\n",
        "        obs_tensor, self._active_set_tensor, masked=True,\n",
        "        aggregate=self._combine_ensemble_method)  # B x A\n",
        "    active_set_to_goal_dist = self._get_pairwise_dist(\n",
        "        self._active_set_tensor, goal_tensor, masked=True,\n",
        "        aggregate=self._combine_ensemble_method)  # A x B\n",
        "\n",
        "    # The search_dist tensor should be (B x A x A)\n",
        "    search_dist = sum([\n",
        "        tf.expand_dims(obs_to_active_set_dist, 2),\n",
        "        tf.expand_dims(self._distances_tensor, 0),\n",
        "        tf.expand_dims(tf.transpose(active_set_to_goal_dist), axis=1)\n",
        "    ])\n",
        "\n",
        "    # We assume a batch size of 1.\n",
        "    assert obs_tensor.shape[0] == 1\n",
        "    min_search_dist = tf.reduce_min(search_dist, axis=[1, 2])[0]\n",
        "    waypoint_index = tf.argmin(tf.reduce_min(search_dist, axis=[2]), axis=1)[0]\n",
        "    waypoint = self._active_set_tensor[waypoint_index]\n",
        "\n",
        "    return waypoint, min_search_dist\n",
        "\n",
        "  def _initialize(self):\n",
        "    for ensemble_index in range(self._ensemble_size):\n",
        "      common.soft_variables_update(\n",
        "          self._critic_network_list[ensemble_index].variables,\n",
        "          self._target_critic_network_list[ensemble_index].variables,\n",
        "          tau=1.0)\n",
        "    # Caution: actor should only be updated once.\n",
        "    common.soft_variables_update(\n",
        "        self._actor_network.variables,\n",
        "        self._target_actor_network.variables,\n",
        "        tau=1.0)\n",
        "\n",
        "  def _get_target_updater(self, tau=1.0, period=1):\n",
        "    \"\"\"Performs a soft update of the target network parameters.\n",
        "\n",
        "    For each weight w_s in the original network, and its corresponding\n",
        "    weight w_t in the target network, a soft update is:\n",
        "    w_t = (1- tau) x w_t + tau x ws\n",
        "\n",
        "    Args:\n",
        "      tau: A float scalar in [0, 1]. Default `tau=1.0` means hard update.\n",
        "      period: Step interval at which the target networks are updated.\n",
        "    Returns:\n",
        "      An operation that performs a soft update of the target network parameters.\n",
        "    \"\"\"\n",
        "    with tf.compat.v1.name_scope('get_target_updater'):\n",
        "      def update():  # pylint: disable=missing-docstring\n",
        "        critic_update_list = []\n",
        "        for ensemble_index in range(self._ensemble_size):\n",
        "          critic_update = common.soft_variables_update(\n",
        "              self._critic_network_list[ensemble_index].variables,\n",
        "              self._target_critic_network_list[ensemble_index].variables, tau)\n",
        "          critic_update_list.append(critic_update)\n",
        "        actor_update = common.soft_variables_update(\n",
        "            self._actor_network.variables,\n",
        "            self._target_actor_network.variables, tau)\n",
        "        return tf.group(critic_update_list + [actor_update])\n",
        "\n",
        "      return common.Periodically(update, period, 'periodic_update_targets')\n",
        "\n",
        "  def _experience_to_transitions(self, experience):\n",
        "    transitions = trajectory.to_transition(experience)\n",
        "    transitions = tf.nest.map_structure(lambda x: tf.squeeze(x, [1]),\n",
        "                                        transitions)\n",
        "\n",
        "    time_steps, policy_steps, next_time_steps = transitions\n",
        "    actions = policy_steps.action\n",
        "    return time_steps, actions, next_time_steps\n",
        "\n",
        "  def _train(self, experience, weights=None):\n",
        "    del weights\n",
        "    time_steps, actions, next_time_steps = self._experience_to_transitions(\n",
        "        experience)\n",
        "\n",
        "    # Update the critic\n",
        "    critic_vars = []\n",
        "    for ensemble_index in range(self._ensemble_size):\n",
        "      critic_net = self._critic_network_list[ensemble_index]\n",
        "      critic_vars.extend(critic_net.variables)\n",
        "\n",
        "    with tf.GradientTape(watch_accessed_variables=False) as tape:\n",
        "      assert critic_vars\n",
        "      tape.watch(critic_vars)\n",
        "      critic_loss = self.critic_loss(time_steps, actions, next_time_steps)\n",
        "    tf.debugging.check_numerics(critic_loss, 'Critic loss is inf or nan.')\n",
        "    critic_grads = tape.gradient(critic_loss, critic_vars)\n",
        "    self._apply_gradients(critic_grads, critic_vars,\n",
        "                          self._critic_optimizer)\n",
        "\n",
        "    # Update the actor\n",
        "    actor_vars = self._actor_network.variables\n",
        "    with tf.GradientTape(watch_accessed_variables=False) as tape:\n",
        "      assert actor_vars, 'No actor variables to optimize.'\n",
        "      tape.watch(actor_vars)\n",
        "      actor_loss = self.actor_loss(time_steps)\n",
        "    tf.debugging.check_numerics(actor_loss, 'Actor loss is inf or nan.')\n",
        "    actor_grads = tape.gradient(actor_loss, actor_vars)\n",
        "    self._apply_gradients(actor_grads, actor_vars, self._actor_optimizer)\n",
        "\n",
        "    self.train_step_counter.assign_add(1)\n",
        "    self._update_target()\n",
        "    total_loss = actor_loss + critic_loss\n",
        "    return tf_agent.LossInfo(total_loss, (actor_loss, critic_loss))\n",
        "\n",
        "  def _apply_gradients(self, gradients, variables, optimizer):\n",
        "    # Tuple is used for py3, where zip is a generator producing values once.\n",
        "    grads_and_vars = tuple(zip(gradients, variables))\n",
        "    optimizer.apply_gradients(grads_and_vars)\n",
        "\n",
        "  def critic_loss(self,\n",
        "                  time_steps,\n",
        "                  actions,\n",
        "                  next_time_steps):\n",
        "    \"\"\"Computes the critic loss for UvfAgent training.\n",
        "\n",
        "    Args:\n",
        "      time_steps: A batch of timesteps.\n",
        "      actions: A batch of actions.\n",
        "      next_time_steps: A batch of next timesteps.\n",
        "    Returns:\n",
        "      critic_loss: A scalar critic loss.\n",
        "    \"\"\"\n",
        "    with tf.compat.v1.name_scope('critic_loss'):\n",
        "      # We compute the target actions once for all critics.\n",
        "      target_actions, _ = self._target_actor_network(\n",
        "          next_time_steps.observation, next_time_steps.step_type)\n",
        "\n",
        "      critic_loss_list = []\n",
        "      q_values_list = self._get_critic_output(self._critic_network_list,\n",
        "                                              time_steps, actions)\n",
        "      target_q_values_list = self._get_critic_output(\n",
        "          self._target_critic_network_list, next_time_steps, target_actions)\n",
        "      assert len(target_q_values_list) == self._ensemble_size\n",
        "      for ensemble_index in range(self._ensemble_size):\n",
        "        # The target_q_values should be a Batch x ensemble_size tensor.\n",
        "        target_q_values = target_q_values_list[ensemble_index]\n",
        "\n",
        "        if self._use_distributional_rl:\n",
        "          target_q_probs = tf.nn.softmax(target_q_values, axis=1)\n",
        "          batch_size = target_q_probs.shape[0]\n",
        "          one_hot = tf.one_hot(tf.zeros(batch_size, dtype=tf.int32),\n",
        "                               self._max_episode_steps)\n",
        "          ### Calculate the shifted probabilities\n",
        "          # Fist column: Since episode didn't terminate, probability that the\n",
        "          # distance is 1 equals 0.\n",
        "          col_1 = tf.zeros((batch_size, 1))\n",
        "          # Middle columns: Simply the shifted probabilities.\n",
        "          col_middle = target_q_probs[:, :-2]\n",
        "          # Last column: Probability of taking at least n steps is sum of\n",
        "          # last two columns in unshifted predictions:\n",
        "          col_last = tf.reduce_sum(target_q_probs[:, -2:], axis=1,\n",
        "                                   keepdims=True)\n",
        "\n",
        "          shifted_target_q_probs = tf.concat([col_1, col_middle, col_last],\n",
        "                                             axis=1)\n",
        "          assert one_hot.shape == shifted_target_q_probs.shape\n",
        "          td_targets = tf.compat.v1.where(next_time_steps.is_last(),\n",
        "                                one_hot,\n",
        "                                shifted_target_q_probs)\n",
        "          td_targets = tf.stop_gradient(td_targets)\n",
        "        else:\n",
        "          td_targets = tf.stop_gradient(\n",
        "              next_time_steps.reward +\n",
        "              next_time_steps.discount * target_q_values)\n",
        "\n",
        "        q_values = q_values_list[ensemble_index]\n",
        "        if self._use_distributional_rl:\n",
        "          critic_loss = tf.nn.softmax_cross_entropy_with_logits(\n",
        "              labels=td_targets,\n",
        "              logits=q_values\n",
        "              )\n",
        "        else:\n",
        "          critic_loss = common.element_wise_huber_loss(td_targets, q_values)\n",
        "        critic_loss = tf.reduce_mean(critic_loss)\n",
        "        critic_loss_list.append(critic_loss)\n",
        "\n",
        "      critic_loss = tf.reduce_mean(critic_loss_list)\n",
        "\n",
        "      return critic_loss\n",
        "\n",
        "  def actor_loss(self, time_steps):\n",
        "    \"\"\"Computes the actor_loss for UvfAgent training.\n",
        "\n",
        "    Args:\n",
        "      time_steps: A batch of timesteps.\n",
        "    Returns:\n",
        "      actor_loss: A scalar actor loss.\n",
        "    \"\"\"\n",
        "    with tf.compat.v1.name_scope('actor_loss'):\n",
        "      actions, _ = self._actor_network(time_steps.observation,\n",
        "                                       time_steps.step_type)\n",
        "      with tf.GradientTape(watch_accessed_variables=False) as tape:\n",
        "        tape.watch(actions)\n",
        "        avg_expected_q_values = self._get_state_values(time_steps, actions,\n",
        "                                                       aggregate='mean')\n",
        "        actions = tf.nest.flatten(actions)\n",
        "      dqdas = tape.gradient([avg_expected_q_values], actions)\n",
        "\n",
        "      actor_losses = []\n",
        "      for dqda, action in zip(dqdas, actions):\n",
        "        loss = common.element_wise_squared_loss(\n",
        "            tf.stop_gradient(dqda + action), action)\n",
        "        loss = tf.reduce_sum(loss, axis=1)\n",
        "        loss = tf.reduce_mean(loss)\n",
        "        actor_losses.append(loss)\n",
        "\n",
        "      actor_loss = tf.add_n(actor_losses)\n",
        "\n",
        "      with tf.compat.v1.name_scope('Losses/'):\n",
        "        tf.compat.v2.summary.scalar(\n",
        "            name='actor_loss', data=actor_loss, step=self.train_step_counter)\n",
        "\n",
        "    return actor_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "MbGXO1TURVf-",
        "colab": {}
      },
      "source": [
        "#@title Training script.\n",
        "def train_eval(\n",
        "    tf_agent,\n",
        "    tf_env,\n",
        "    eval_tf_env,\n",
        "    num_iterations=2000000,\n",
        "    # Params for collect\n",
        "    initial_collect_steps=1000,\n",
        "    batch_size=64,\n",
        "    # Params for eval\n",
        "    num_eval_episodes=100,\n",
        "    eval_interval=10000,\n",
        "    # Params for checkpoints, summaries, and logging\n",
        "    log_interval=1000,\n",
        "    random_seed=0):\n",
        "  \"\"\"A simple train and eval for UVF.  \"\"\"\n",
        "  tf.compat.v1.logging.info('random_seed = %d' % random_seed)\n",
        "  np.random.seed(random_seed)\n",
        "  random.seed(random_seed)\n",
        "  tf.compat.v1.set_random_seed(random_seed)\n",
        "  \n",
        "  max_episode_steps = tf_env.pyenv.envs[0]._duration\n",
        "  global_step = tf.compat.v1.train.get_or_create_global_step()\n",
        "  replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(\n",
        "      tf_agent.collect_data_spec,\n",
        "      batch_size=tf_env.batch_size)\n",
        "\n",
        "  eval_metrics = [\n",
        "    tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),\n",
        "  ]\n",
        "  \n",
        "  eval_policy = tf_agent.policy\n",
        "  collect_policy = tf_agent.collect_policy\n",
        "  initial_collect_driver = dynamic_step_driver.DynamicStepDriver(\n",
        "      tf_env,\n",
        "      collect_policy,\n",
        "      observers=[replay_buffer.add_batch],\n",
        "      num_steps=initial_collect_steps)\n",
        "  \n",
        "  collect_driver = dynamic_step_driver.DynamicStepDriver(\n",
        "      tf_env,\n",
        "      collect_policy,\n",
        "      observers=[replay_buffer.add_batch],\n",
        "      num_steps=1)\n",
        "  \n",
        "  initial_collect_driver.run = common.function(initial_collect_driver.run)\n",
        "  collect_driver.run = common.function(collect_driver.run)\n",
        "  tf_agent.train = common.function(tf_agent.train)\n",
        "  \n",
        "  initial_collect_driver.run()\n",
        "  \n",
        "  time_step = None\n",
        "  policy_state = collect_policy.get_initial_state(tf_env.batch_size)\n",
        "\n",
        "  timed_at_step = global_step.numpy()\n",
        "  time_acc = 0\n",
        "\n",
        "  # Dataset generates trajectories with shape [Bx2x...]\n",
        "  dataset = replay_buffer.as_dataset(\n",
        "      num_parallel_calls=3,\n",
        "      sample_batch_size=batch_size,\n",
        "      num_steps=2).prefetch(3)\n",
        "  iterator = iter(dataset)\n",
        "  \n",
        "  for _ in tqdm.tnrange(num_iterations):\n",
        "    start_time = time.time()\n",
        "    time_step, policy_state = collect_driver.run(\n",
        "        time_step=time_step,\n",
        "        policy_state=policy_state,\n",
        "    )\n",
        "    \n",
        "    experience, _ = next(iterator)\n",
        "    train_loss = tf_agent.train(experience)\n",
        "    time_acc += time.time() - start_time\n",
        "\n",
        "    if global_step.numpy() % log_interval == 0:\n",
        "      tf.compat.v1.logging.info('step = %d, loss = %f', global_step.numpy(),\n",
        "                    train_loss.loss)\n",
        "      steps_per_sec = log_interval / time_acc\n",
        "      tf.compat.v1.logging.info('%.3f steps/sec', steps_per_sec)\n",
        "      time_acc = 0\n",
        "\n",
        "    if global_step.numpy() % eval_interval == 0:\n",
        "      start = time.time()\n",
        "      tf.compat.v1.logging.info('step = %d' % global_step.numpy())\n",
        "      for dist in [2, 5, 10]:\n",
        "        tf.compat.v1.logging.info('\\t dist = %d' % dist)\n",
        "        eval_tf_env.pyenv.envs[0].gym.set_sample_goal_args(\n",
        "          prob_constraint=1.0, min_dist=dist-1, max_dist=dist+1)\n",
        "\n",
        "        results = metric_utils.eager_compute(\n",
        "            eval_metrics,\n",
        "            eval_tf_env,\n",
        "            eval_policy,\n",
        "            num_episodes=num_eval_episodes,\n",
        "            train_step=global_step,\n",
        "            summary_prefix='Metrics',\n",
        "        )\n",
        "        for (key, value) in results.items():\n",
        "          tf.compat.v1.logging.info('\\t\\t %s = %.2f', key, value.numpy())\n",
        "        # For debugging, it's helpful to check the predicted distances for\n",
        "        # goals of known distance.\n",
        "        pred_dist = []\n",
        "        for _ in range(num_eval_episodes):\n",
        "          ts = eval_tf_env.reset()\n",
        "          dist_to_goal = agent._get_dist_to_goal(ts)[0]\n",
        "          pred_dist.append(dist_to_goal.numpy())\n",
        "        tf.compat.v1.logging.info('\\t\\t predicted_dist = %.1f (%.1f)' %\n",
        "                        (np.mean(pred_dist), np.std(pred_dist)))\n",
        "      tf.compat.v1.logging.info('\\t eval_time = %.2f' % (time.time() - start))\n",
        "        \n",
        "  return train_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zxW1bAAPeOH6"
      },
      "source": [
        "----------------\n",
        "## Train the Agent!\n",
        "Now we're going to train the goal-conditioned RL agent. The first cell resets the weights, and the second cell does the actual training. If you want to continue training for longer, simply run the second cell again. Expect training to take about 10 minutes. For some of the complex environments, you may need to increase the `num_iterations` to 100,000."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lEBCYVpqU07z",
        "colab": {}
      },
      "source": [
        "# Run this cell before training on a new environment!\n",
        "tf.compat.v1.reset_default_graph()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4mmoO3zoSGVE",
        "outputId": "b4a3310a-574d-4783-dcc6-20e2fb316b13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# If you change the environment parameters below, make sure to run\n",
        "# tf.reset_default_graph() in the cell above before training.\n",
        "max_episode_steps = 20\n",
        "env_name = 'FourRooms'  # Choose one of the environments shown above. \n",
        "resize_factor = 5  # Inflate the environment to increase the difficulty.\n",
        "\n",
        "tf_env = env_load_fn(env_name, max_episode_steps,\n",
        "                     resize_factor=resize_factor,\n",
        "                     terminate_on_timeout=False)\n",
        "eval_tf_env = env_load_fn(env_name, max_episode_steps,\n",
        "                          resize_factor=resize_factor,\n",
        "                          terminate_on_timeout=True)\n",
        "agent = UvfAgent(\n",
        "    tf_env.time_step_spec(),\n",
        "    tf_env.action_spec(),\n",
        "    max_episode_steps=max_episode_steps,\n",
        "    use_distributional_rl=True,\n",
        "    ensemble_size=3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
            "  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiWBz2nPugCK",
        "colab_type": "code",
        "outputId": "3f7e06bf-2c9e-44df-ca64-3fe676ca56dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "b5d332fab17740d995366e405df4d3c0",
            "622a15da4cf54bd68b93fd57da09fc6f",
            "8d82ba56921c44b5b5f3434db8567f95",
            "443305604f2549dfa947dc45c933bab6",
            "d6f104d812484af4810aba83ff8d884e",
            "ff3740d3a7f546b09fd0fd3d73973db7",
            "45cb46435c6f485dae99709168e83339",
            "d0c89a27b5b647d4a7b6484f40c332d6"
          ]
        }
      },
      "source": [
        "train_eval(\n",
        "    agent,\n",
        "    tf_env,\n",
        "    eval_tf_env,\n",
        "    initial_collect_steps=1000,\n",
        "    eval_interval=1000,\n",
        "    num_eval_episodes=10,\n",
        "    num_iterations=30000,\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:random_seed = 0\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:64: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b5d332fab17740d995366e405df4d3c0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=30000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:step = 1000, loss = 2.162520\n",
            "INFO:tensorflow:97.453 steps/sec\n",
            "INFO:tensorflow:step = 1000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 16.3 (0.4)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 16.4 (0.5)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.20\n",
            "INFO:tensorflow:\t\t predicted_dist = 16.5 (0.4)\n",
            "INFO:tensorflow:\t eval_time = 2.54\n",
            "INFO:tensorflow:step = 2000, loss = 0.956672\n",
            "INFO:tensorflow:116.785 steps/sec\n",
            "INFO:tensorflow:step = 2000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 16.8 (1.1)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -17.80\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.5 (0.7)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.20\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.4 (0.4)\n",
            "INFO:tensorflow:\t eval_time = 2.02\n",
            "INFO:tensorflow:step = 3000, loss = 0.830508\n",
            "INFO:tensorflow:116.410 steps/sec\n",
            "INFO:tensorflow:step = 3000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.0 (0.4)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.5 (0.2)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.5 (0.4)\n",
            "INFO:tensorflow:\t eval_time = 2.04\n",
            "INFO:tensorflow:step = 4000, loss = 0.582359\n",
            "INFO:tensorflow:118.107 steps/sec\n",
            "INFO:tensorflow:step = 4000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -12.90\n",
            "INFO:tensorflow:\t\t predicted_dist = 19.0 (0.3)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.50\n",
            "INFO:tensorflow:\t\t predicted_dist = 19.0 (0.6)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.80\n",
            "INFO:tensorflow:\t\t predicted_dist = 19.3 (0.2)\n",
            "INFO:tensorflow:\t eval_time = 1.98\n",
            "INFO:tensorflow:step = 5000, loss = 0.433917\n",
            "INFO:tensorflow:119.619 steps/sec\n",
            "INFO:tensorflow:step = 5000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -13.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.9 (0.7)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.8 (0.7)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 19.4 (0.4)\n",
            "INFO:tensorflow:\t eval_time = 1.99\n",
            "INFO:tensorflow:step = 6000, loss = 1.116411\n",
            "INFO:tensorflow:119.433 steps/sec\n",
            "INFO:tensorflow:step = 6000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.4 (0.9)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.9 (0.7)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.3 (1.0)\n",
            "INFO:tensorflow:\t eval_time = 2.07\n",
            "INFO:tensorflow:step = 7000, loss = 1.208330\n",
            "INFO:tensorflow:119.236 steps/sec\n",
            "INFO:tensorflow:step = 7000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.30\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.1 (0.8)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.8 (0.3)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.6 (0.2)\n",
            "INFO:tensorflow:\t eval_time = 2.03\n",
            "INFO:tensorflow:step = 8000, loss = 1.039809\n",
            "INFO:tensorflow:118.386 steps/sec\n",
            "INFO:tensorflow:step = 8000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 15.0 (1.8)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.0 (0.5)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.2 (0.5)\n",
            "INFO:tensorflow:\t eval_time = 2.08\n",
            "INFO:tensorflow:step = 9000, loss = 1.565062\n",
            "INFO:tensorflow:119.017 steps/sec\n",
            "INFO:tensorflow:step = 9000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -14.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 16.4 (1.6)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.8 (0.5)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.3 (0.4)\n",
            "INFO:tensorflow:\t eval_time = 2.04\n",
            "INFO:tensorflow:step = 10000, loss = 1.175910\n",
            "INFO:tensorflow:119.405 steps/sec\n",
            "INFO:tensorflow:step = 10000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 14.8 (2.8)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -17.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.7 (0.9)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -20.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 18.4 (0.5)\n",
            "INFO:tensorflow:\t eval_time = 2.05\n",
            "INFO:tensorflow:step = 11000, loss = 1.550883\n",
            "INFO:tensorflow:118.824 steps/sec\n",
            "INFO:tensorflow:step = 11000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -13.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.3 (3.3)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.20\n",
            "INFO:tensorflow:\t\t predicted_dist = 16.4 (1.0)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.30\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.9 (0.5)\n",
            "INFO:tensorflow:\t eval_time = 1.98\n",
            "INFO:tensorflow:step = 12000, loss = 1.892312\n",
            "INFO:tensorflow:118.782 steps/sec\n",
            "INFO:tensorflow:step = 12000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.9 (1.9)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.90\n",
            "INFO:tensorflow:\t\t predicted_dist = 15.4 (1.1)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.5 (0.4)\n",
            "INFO:tensorflow:\t eval_time = 1.97\n",
            "INFO:tensorflow:step = 13000, loss = 2.081666\n",
            "INFO:tensorflow:118.507 steps/sec\n",
            "INFO:tensorflow:step = 13000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -10.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.0 (2.4)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -12.20\n",
            "INFO:tensorflow:\t\t predicted_dist = 15.3 (1.2)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.50\n",
            "INFO:tensorflow:\t\t predicted_dist = 16.9 (1.0)\n",
            "INFO:tensorflow:\t eval_time = 1.76\n",
            "INFO:tensorflow:step = 14000, loss = 1.849810\n",
            "INFO:tensorflow:117.296 steps/sec\n",
            "INFO:tensorflow:step = 14000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -11.20\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.3 (2.1)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -11.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 15.2 (0.8)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -19.80\n",
            "INFO:tensorflow:\t\t predicted_dist = 17.0 (0.5)\n",
            "INFO:tensorflow:\t eval_time = 1.94\n",
            "INFO:tensorflow:step = 15000, loss = 2.549021\n",
            "INFO:tensorflow:117.630 steps/sec\n",
            "INFO:tensorflow:step = 15000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -7.90\n",
            "INFO:tensorflow:\t\t predicted_dist = 10.8 (1.7)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -13.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 13.5 (0.9)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.90\n",
            "INFO:tensorflow:\t\t predicted_dist = 15.1 (0.5)\n",
            "INFO:tensorflow:\t eval_time = 1.75\n",
            "INFO:tensorflow:step = 16000, loss = 2.519606\n",
            "INFO:tensorflow:117.362 steps/sec\n",
            "INFO:tensorflow:step = 16000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -9.30\n",
            "INFO:tensorflow:\t\t predicted_dist = 10.0 (1.7)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -12.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 13.3 (1.4)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 15.8 (0.7)\n",
            "INFO:tensorflow:\t eval_time = 1.77\n",
            "INFO:tensorflow:step = 17000, loss = 2.431136\n",
            "INFO:tensorflow:118.151 steps/sec\n",
            "INFO:tensorflow:step = 17000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -5.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 9.2 (1.8)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -9.50\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.4 (1.1)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.80\n",
            "INFO:tensorflow:\t\t predicted_dist = 15.4 (1.0)\n",
            "INFO:tensorflow:\t eval_time = 1.55\n",
            "INFO:tensorflow:step = 18000, loss = 2.314609\n",
            "INFO:tensorflow:117.378 steps/sec\n",
            "INFO:tensorflow:step = 18000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -6.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 8.5 (1.6)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -10.30\n",
            "INFO:tensorflow:\t\t predicted_dist = 11.2 (1.3)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.80\n",
            "INFO:tensorflow:\t\t predicted_dist = 14.6 (1.0)\n",
            "INFO:tensorflow:\t eval_time = 1.63\n",
            "INFO:tensorflow:step = 19000, loss = 2.724467\n",
            "INFO:tensorflow:117.963 steps/sec\n",
            "INFO:tensorflow:step = 19000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -2.80\n",
            "INFO:tensorflow:\t\t predicted_dist = 8.2 (1.8)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -7.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 10.7 (1.5)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.90\n",
            "INFO:tensorflow:\t\t predicted_dist = 14.4 (0.7)\n",
            "INFO:tensorflow:\t eval_time = 1.46\n",
            "INFO:tensorflow:step = 20000, loss = 2.762258\n",
            "INFO:tensorflow:117.131 steps/sec\n",
            "INFO:tensorflow:step = 20000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -3.30\n",
            "INFO:tensorflow:\t\t predicted_dist = 7.3 (2.0)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -8.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 10.7 (1.0)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 13.4 (0.9)\n",
            "INFO:tensorflow:\t eval_time = 1.52\n",
            "INFO:tensorflow:step = 21000, loss = 2.744784\n",
            "INFO:tensorflow:117.122 steps/sec\n",
            "INFO:tensorflow:step = 21000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -4.50\n",
            "INFO:tensorflow:\t\t predicted_dist = 6.0 (1.7)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -8.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 11.2 (0.8)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -18.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 13.0 (0.6)\n",
            "INFO:tensorflow:\t eval_time = 1.58\n",
            "INFO:tensorflow:step = 22000, loss = 3.057208\n",
            "INFO:tensorflow:117.479 steps/sec\n",
            "INFO:tensorflow:step = 22000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -2.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 5.7 (1.1)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -10.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 9.5 (1.1)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.5 (0.8)\n",
            "INFO:tensorflow:\t eval_time = 1.56\n",
            "INFO:tensorflow:step = 23000, loss = 2.963032\n",
            "INFO:tensorflow:117.205 steps/sec\n",
            "INFO:tensorflow:step = 23000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -3.50\n",
            "INFO:tensorflow:\t\t predicted_dist = 5.8 (1.9)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -8.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 10.0 (1.0)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.50\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.1 (0.5)\n",
            "INFO:tensorflow:\t eval_time = 1.51\n",
            "INFO:tensorflow:step = 24000, loss = 2.867639\n",
            "INFO:tensorflow:116.640 steps/sec\n",
            "INFO:tensorflow:step = 24000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -3.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 6.5 (1.5)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -7.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 9.7 (1.2)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.4 (1.1)\n",
            "INFO:tensorflow:\t eval_time = 1.48\n",
            "INFO:tensorflow:step = 25000, loss = 2.749675\n",
            "INFO:tensorflow:117.961 steps/sec\n",
            "INFO:tensorflow:step = 25000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -5.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 4.7 (1.9)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -9.10\n",
            "INFO:tensorflow:\t\t predicted_dist = 9.3 (0.8)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 13.0 (1.4)\n",
            "INFO:tensorflow:\t eval_time = 1.56\n",
            "INFO:tensorflow:step = 26000, loss = 2.804070\n",
            "INFO:tensorflow:118.313 steps/sec\n",
            "INFO:tensorflow:step = 26000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -4.30\n",
            "INFO:tensorflow:\t\t predicted_dist = 7.1 (1.7)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -8.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 10.8 (0.8)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.40\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.8 (0.8)\n",
            "INFO:tensorflow:\t eval_time = 1.46\n",
            "INFO:tensorflow:step = 27000, loss = 3.136049\n",
            "INFO:tensorflow:118.855 steps/sec\n",
            "INFO:tensorflow:step = 27000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -3.30\n",
            "INFO:tensorflow:\t\t predicted_dist = 6.5 (1.9)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -7.60\n",
            "INFO:tensorflow:\t\t predicted_dist = 9.7 (1.1)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -14.50\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.9 (1.1)\n",
            "INFO:tensorflow:\t eval_time = 1.51\n",
            "INFO:tensorflow:step = 28000, loss = 3.073531\n",
            "INFO:tensorflow:116.729 steps/sec\n",
            "INFO:tensorflow:step = 28000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -5.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 5.7 (2.0)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -8.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 8.9 (1.1)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -15.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.3 (1.2)\n",
            "INFO:tensorflow:\t eval_time = 1.55\n",
            "INFO:tensorflow:step = 29000, loss = 3.034347\n",
            "INFO:tensorflow:118.716 steps/sec\n",
            "INFO:tensorflow:step = 29000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -3.90\n",
            "INFO:tensorflow:\t\t predicted_dist = 6.6 (2.1)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -8.20\n",
            "INFO:tensorflow:\t\t predicted_dist = 8.9 (1.4)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.70\n",
            "INFO:tensorflow:\t\t predicted_dist = 12.5 (0.7)\n",
            "INFO:tensorflow:\t eval_time = 1.52\n",
            "INFO:tensorflow:step = 30000, loss = 3.413032\n",
            "INFO:tensorflow:117.119 steps/sec\n",
            "INFO:tensorflow:step = 30000\n",
            "INFO:tensorflow:\t dist = 2\n",
            "INFO:tensorflow:\t\t AverageReturn = -4.00\n",
            "INFO:tensorflow:\t\t predicted_dist = 5.9 (1.5)\n",
            "INFO:tensorflow:\t dist = 5\n",
            "INFO:tensorflow:\t\t AverageReturn = -8.20\n",
            "INFO:tensorflow:\t\t predicted_dist = 9.5 (1.0)\n",
            "INFO:tensorflow:\t dist = 10\n",
            "INFO:tensorflow:\t\t AverageReturn = -16.80\n",
            "INFO:tensorflow:\t\t predicted_dist = 11.7 (0.6)\n",
            "INFO:tensorflow:\t eval_time = 1.58\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LossInfo(loss=<tf.Tensor: shape=(), dtype=float32, numpy=3.413032>, extra=(<tf.Tensor: shape=(), dtype=float32, numpy=1.0606934>, <tf.Tensor: shape=(), dtype=float32, numpy=2.3523386>))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KxFFTj3Ynv03"
      },
      "source": [
        "-------\n",
        "## Visualization\n",
        "Now, let's visualize some rollouts from the learned policy. Below, you can change the difficulty of the task, which moves the goals closer or further from the starting location. You can change the distance to the goal using the slider below. Notice that, if the goals are nearby, the agent can always reach them. If the goals are far away, the agent rarely reaches them. If only we could lay down a set of \"breadcrumbs\" that led the agent to the goal..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "ZVmiN6xpvNaL",
        "outputId": "da23d725-67a5-4826-b400-d80caf2dae12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480,
          "referenced_widgets": [
            "632a131024b2451482d9173d105bd823",
            "705b9b0cd31c40689c8397821c9618f5",
            "078d18160948461d8579b9dd9f11877b",
            "32d1f9ecf0f4440598e9443fbc3b2446",
            "70d3947661eb472ea22d757c14fd3c06",
            "be808c127265415284212a1dfccb739d",
            "daff913f827f4d67b1a7b23332b56814",
            "98bb1f1899f64976ba5ba838efe16939",
            "48ce5a97482841409f0337f0e70e664b",
            "3e733ef9a9dd4550ae3e3276de04934e",
            "59153e72ac404ca78e48240744d818e2",
            "c647d67ef51842b3b1bead3444ce6a15",
            "745a60af87f8413baab83c4fdb5be006",
            "788be724892b42e48e0e729b6799a936",
            "4b60a4715ca84fe3b813f60d5107a250",
            "19fbcc7d81604f6b97f81512ee15fe90"
          ]
        }
      },
      "source": [
        "#@title Visualize rollouts. {run: \"auto\" }\n",
        "eval_tf_env.pyenv.envs[0]._duration = 100  # We'll give the agent lots of time to try to find the goal.\n",
        "difficulty = 0.7 #@param {min:0, max: 1, step: 0.1, type:\"slider\"}\n",
        "max_goal_dist = eval_tf_env.pyenv.envs[0].gym.max_goal_dist\n",
        "eval_tf_env.pyenv.envs[0].gym.set_sample_goal_args(\n",
        "    prob_constraint=1.0,\n",
        "    min_dist=max(0, max_goal_dist * (difficulty - 0.05)),\n",
        "    max_dist=max_goal_dist * (difficulty + 0.05))\n",
        "\n",
        "\n",
        "def get_rollout(tf_env, policy, seed=None):\n",
        "  np.random.seed(seed)  # Use the same task for both policies.\n",
        "  obs_vec = []\n",
        "  waypoint_vec = []\n",
        "  ts = tf_env.reset()\n",
        "  goal = ts.observation['goal'].numpy()[0]\n",
        "  for _ in tqdm.tnrange(tf_env.pyenv.envs[0]._duration):\n",
        "    obs_vec.append(ts.observation['observation'].numpy()[0])\n",
        "    action = policy.action(ts)\n",
        "    waypoint_vec.append(ts.observation['goal'].numpy()[0])\n",
        "    ts = tf_env.step(action)\n",
        "    if ts.is_last():\n",
        "      break\n",
        "  obs_vec.append(ts.observation['observation'].numpy()[0])\n",
        "  obs_vec = np.array(obs_vec)\n",
        "  waypoint_vec = np.array(waypoint_vec)\n",
        "  return obs_vec, goal, waypoint_vec\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "for col_index in range(2):\n",
        "  plt.subplot(1, 2, col_index + 1)\n",
        "  plot_walls(eval_tf_env.pyenv.envs[0].env.walls)\n",
        "  obs_vec, goal, _ = get_rollout(eval_tf_env, agent.policy)\n",
        "\n",
        "  plt.plot(obs_vec[:, 0], obs_vec[:, 1], 'b-o', alpha=0.3)\n",
        "  plt.scatter([obs_vec[0, 0]], [obs_vec[0, 1]], marker='+',\n",
        "              color='red', s=200, label='start')\n",
        "  plt.scatter([obs_vec[-1, 0]], [obs_vec[-1, 1]], marker='+',\n",
        "              color='green', s=200, label='end')\n",
        "  plt.scatter([goal[0]], [goal[1]], marker='*',\n",
        "              color='green', s=200, label='goal')\n",
        "  if col_index == 0:\n",
        "    plt.legend(loc='lower left', bbox_to_anchor=(0.3, 1), ncol=3, fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "632a131024b2451482d9173d105bd823",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48ce5a97482841409f0337f0e70e664b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "llYSSGRVoIDj"
      },
      "source": [
        "We now will implement the search policy, which automatically finds these waypoints via graph search. The first step is to fill the replay buffer with random data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "6-TCcL5Q9Kn_",
        "outputId": "9a5d0cc4-d56f-4de4-9c35-7ebe502f38da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467,
          "referenced_widgets": [
            "fab883706acd405bb0f75201ba2709a0",
            "23309538adce42c7827ef26584956a3c",
            "90de69d304f8452d92f642ce7b70ef66",
            "064370da316a4bfdb261917f280a3e46",
            "c488d9b2b03e4fe8a3320724c9f4ab09",
            "29fdac21bb004be38083b48097838bef",
            "5242860e28214a3f8ad3e05b30eb6f03",
            "716212cc429c4ee78b430549a2b24c15"
          ]
        }
      },
      "source": [
        "#@title Fill the replay buffer with random data  {vertical-output: true, run: \"auto\" }\n",
        "replay_buffer_size = 1000 #@param {min:100, max: 1000, step: 100, type:\"slider\"}\n",
        "\n",
        "eval_tf_env.pyenv.envs[0].gym.set_sample_goal_args(\n",
        "    prob_constraint=0.0,\n",
        "    min_dist=0,\n",
        "    max_dist=np.inf)\n",
        "rb_vec = []\n",
        "for _ in tqdm.tnrange(replay_buffer_size):\n",
        "  ts = eval_tf_env.reset()\n",
        "  rb_vec.append(ts.observation['observation'].numpy()[0])\n",
        "rb_vec = np.array(rb_vec)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.scatter(*rb_vec.T)\n",
        "plot_walls(eval_tf_env.pyenv.envs[0].env.walls)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fab883706acd405bb0f75201ba2709a0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yhW1jzfNoR4w"
      },
      "source": [
        "As a sanity check, we'll plot the pairwise distances between all observations in the replay buffer. We expect to see a range of values from 1 to 20. Distributional RL implicitly caps the maximum predicted distance by the largest bin. We've used 20 bins, so the critic predicts 20 for all states that are at least 20 steps away from one another."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "gIo_CYsZu6Qy",
        "outputId": "92d2ee25-2320-4ee7-e919-b59e57f81940",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "#@title Compute the pairwise distances { vertical-output: true}\n",
        "pdist = agent._get_pairwise_dist(rb_vec, aggregate=None).numpy()\n",
        "plt.figure(figsize=(6, 3))\n",
        "plt.hist(pdist.flatten(), bins=range(20))\n",
        "plt.xlabel('predicted distance')\n",
        "plt.ylabel('number of (s, g) pairs')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bWVW45bzozca"
      },
      "source": [
        "With these distances, we can construct a graph. Nodes in the graph are observations in our replay buffer. We connect observations with edges whose lengths are equal to the predicted distance between those observations. Since it is hard to visualize the edge lengths, we included a slider that allows you to only show edges whose predicted length is less than some threshold.\n",
        "\n",
        "Our method learns a collection of critics, each of which makes an independent prediction for the distance between two states. Because each network may make bad predictions for pairs of states it hasn't seen before, we act in a *risk-averse* manner by using the maximum predicted distance across our ensemble. That is, we act pessimistically, only adding an edge if *all* critics think that this pair of states is nearby. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "30X6CutHy_9W",
        "outputId": "67925748-7106-4cef-9324-fd12957a76cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485,
          "referenced_widgets": [
            "51bb1dbc57544211bc3e3a32d8bcb565",
            "9b3edc9bc76a4fe6b7b9d5e5bea4c545",
            "9a217f9048e74ce6a05d85c266c3f003",
            "d39735f0c9684b35a51b5e83c83e9519",
            "f5386df2980e4aa5a24977d5c056dd92",
            "df9543ebc5774a3d9f97d5a7755fba71",
            "433305403b934c2388181858ca20ab39",
            "2862c46bf3af48e390afca53f03e88d3"
          ]
        }
      },
      "source": [
        "#@title Graph Construction { vertical-output: true, run: \"auto\" }\n",
        "cutoff = 7 #@param {min:0, max: 20, type:\"slider\"}\n",
        "# To make visualization easier, we only display the shortest edges for each\n",
        "# node. We will use all edges for planning.\n",
        "edges_to_display = 8\n",
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "plot_walls(eval_tf_env.pyenv.envs[0].env.walls)\n",
        "pdist_combined = np.max(pdist, axis=0)\n",
        "plt.scatter(*rb_vec.T)\n",
        "for i, s_i in enumerate(tqdm.tqdm_notebook(rb_vec)):\n",
        "  for count, j in enumerate(np.argsort(pdist_combined[i])):\n",
        "    if count < edges_to_display and pdist_combined[i, j] < cutoff:\n",
        "      s_j = rb_vec[j]\n",
        "      plt.plot([s_i[0], s_j[0]], [s_i[1], s_j[1]], c='k', alpha=0.5)\n",
        "      \n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51bb1dbc57544211bc3e3a32d8bcb565",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZAfUMydwp16_"
      },
      "source": [
        "We can also visualize the predictions from each critic. Note that while each critic may make incorrect decisions for distant states, their predictions in aggregate are correct."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "x_yH1vFNcuRj",
        "outputId": "e60f5f21-bdc1-409d-e71c-2e0b53a03ec0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492,
          "referenced_widgets": [
            "0c6dd0fda8584acf8c51a2d0aacdf9e1",
            "1209ca52843d4cedaa728062b1a3410b",
            "cbad5e291729474f9f3d7867cd5eee50",
            "16ae16e470024658abf603f33caa3033",
            "1446d92b8ac44fc080a8d4d54c876021",
            "3ffe3f5598f24656b73e14cc585ee515",
            "d202c469b1db4f7d8d1c0da6bfe9e29c",
            "61801655a53f49ce885f0e7904481cc8",
            "75543e58b00b47c9befb5cba044efc06",
            "30cd868413eb4396924162cadc995f86",
            "404e503327124a9687d5042790ae3084",
            "b8c7bbd4b2b34540945b333582ad95bb",
            "c3545f6efcfa400d94f96fb31b823180",
            "cb9c3f8ae9ac4b528efebe74214e3c51",
            "3502263a5bd3465e9f74ef4083a4d973",
            "0437e6c0d3eb4870978a89760552d5b3",
            "cda2f2bdd13443e285ecb9dbd5701543",
            "deab5defa0aa4faba1243743c94d5615",
            "8629eea2ae2642e1ae54e5c7f295c24c",
            "5570c4e609df4218a7da84e95a67df3a",
            "8404a6d32d484d77b6bc06c043448ae2",
            "03245744b4cb4377a7f5d6f0c233b49e",
            "88cab50b558a4e389d6acfd7287a6a37",
            "4c5e548d983a4e9bb9339aa7fd8d2d59"
          ]
        }
      },
      "source": [
        "#@title Ensemble of Critics { vertical-output: true, run: \"auto\" }\n",
        "cutoff = 7 #@param {min:0, max: 20, type:\"slider\"}\n",
        "edges_to_display = 8\n",
        "plt.figure(figsize=(15, 4))\n",
        "\n",
        "for col_index in range(agent._ensemble_size):\n",
        "  plt.subplot(1, agent._ensemble_size, col_index + 1)\n",
        "  plot_walls(eval_tf_env.pyenv.envs[0].env.walls)\n",
        "  plt.title('critic %d' % (col_index + 1))\n",
        "\n",
        "  plt.scatter(*rb_vec.T)\n",
        "  desc='critic %d / %d' % (col_index + 1, agent._ensemble_size)\n",
        "  for i, s_i in enumerate(tqdm.tqdm_notebook(rb_vec, desc=desc)):\n",
        "    for count, j in enumerate(np.argsort(pdist[col_index, i])):\n",
        "      if count < edges_to_display and pdist[col_index, i, j] < cutoff:\n",
        "        s_j = rb_vec[j]\n",
        "        plt.plot([s_i[0], s_j[0]], [s_i[1], s_j[1]], c='k', alpha=0.5)\n",
        "      \n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c6dd0fda8584acf8c51a2d0aacdf9e1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='critic 1 / 3', max=1000.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75543e58b00b47c9befb5cba044efc06",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='critic 2 / 3', max=1000.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cda2f2bdd13443e285ecb9dbd5701543",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='critic 3 / 3', max=1000.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NHCpUMJmSDzq"
      },
      "source": [
        "### Search Policy\n",
        "Now, we will combine the goal-conditioned policy and the distance estimates to form a search policy. Internally, this policy performs search over the replay buffer to find a set of waypoints leading to the goal. It then takes actions to reach each waypoint in turn. Because the search happens internally, this policy is a drop-in replacement for any other goal-conditioned policy.\n",
        "\n",
        "We used a *closed loop* policy in our paper, recomputing the search path after each step. Below, we implement both the original closed loop version as well as an *open loop* version, which only performs search once at the start of the episode. The open loop version is much faster, but may perform worse in stochastic environments where replanning is important."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "tXFDgreyOodM",
        "colab": {}
      },
      "source": [
        "#@title Implement the search policy\n",
        "class SearchPolicy(tf_policy.Base):\n",
        "  \n",
        "  def __init__(self, agent, open_loop=False):\n",
        "    self._agent = agent\n",
        "    self._open_loop = open_loop\n",
        "    self._g = self._build_graph()\n",
        "    super(SearchPolicy, self).__init__(agent.policy.time_step_spec,\n",
        "                                       agent.policy.action_spec)\n",
        "    \n",
        "  def _build_graph(self):\n",
        "    g = nx.DiGraph()\n",
        "    pdist_combined = np.max(pdist, axis=0)\n",
        "    for i, s_i in enumerate(rb_vec):\n",
        "      for j, s_j in enumerate(rb_vec):\n",
        "        length = pdist_combined[i, j]\n",
        "        if length < self._agent._max_search_steps:\n",
        "          g.add_edge(i, j, weight=length)\n",
        "    return g\n",
        "  \n",
        "  def _get_path(self, time_step):\n",
        "    start_to_rb = agent._get_pairwise_dist(ts.observation['observation'],\n",
        "                                           rb_vec,\n",
        "                                           aggregate='min',\n",
        "                                           masked=True).numpy().flatten()\n",
        "    rb_to_goal = agent._get_pairwise_dist(rb_vec,\n",
        "                                          ts.observation['goal'],\n",
        "                                          aggregate='min',\n",
        "                                          masked=True).numpy().flatten()\n",
        "\n",
        "    g2 = self._g.copy()\n",
        "    for i, (dist_from_start, dist_to_goal) in enumerate(zip(start_to_rb, rb_to_goal)):\n",
        "      if dist_from_start < self._agent._max_search_steps:\n",
        "        g2.add_edge('start', i, weight=dist_from_start)\n",
        "      if dist_to_goal < self._agent._max_search_steps:\n",
        "        g2.add_edge(i, 'goal', weight=dist_to_goal)\n",
        "    path = nx.shortest_path(g2, 'start', 'goal')\n",
        "    edge_lengths = []\n",
        "    for (i, j) in zip(path[:-1], path[1:]):\n",
        "      edge_lengths.append(g2[i][j]['weight'])\n",
        "    wypt_to_goal_dist = np.cumsum(edge_lengths[::-1])[::-1]  # Reverse CumSum\n",
        "    waypoint_vec = list(path)[1:-1]\n",
        "    return waypoint_vec, wypt_to_goal_dist[1:]\n",
        "    \n",
        "  def _action(self, time_step, policy_state=(), seed=None):\n",
        "    goal = time_step.observation['goal']\n",
        "    dist_to_goal = self._agent._get_dist_to_goal(time_step)[0].numpy()\n",
        "    if self._open_loop:\n",
        "      if time_step.is_first():\n",
        "        self._waypoint_vec, self._wypt_to_goal_dist_vec = self._get_path(time_step)\n",
        "        self._waypoint_counter = 0\n",
        "      waypoint = rb_vec[self._waypoint_vec[self._waypoint_counter]]\n",
        "      time_step.observation['goal'] = waypoint[None]\n",
        "      dist_to_waypoint = self._agent._get_dist_to_goal(time_step)[0].numpy()\n",
        "      if dist_to_waypoint < self._agent._max_search_steps:\n",
        "        self._waypoint_counter = min(self._waypoint_counter + 1,\n",
        "                                   len(self._waypoint_vec) - 1)\n",
        "        waypoint = rb_vec[self._waypoint_vec[self._waypoint_counter]]\n",
        "        time_step.observation['goal'] = waypoint[None]\n",
        "        dist_to_waypoint = self._agent._get_dist_to_goal(time_step._replace())[0].numpy()\n",
        "      dist_to_goal_via_wypt = dist_to_waypoint + self._wypt_to_goal_dist_vec[self._waypoint_counter]\n",
        "    else:\n",
        "      (waypoint, dist_to_goal_via_wypt) = self._agent._get_waypoint(time_step)\n",
        "      dist_to_goal_via_wypt = dist_to_goal_via_wypt.numpy()\n",
        "      \n",
        "    if (dist_to_goal_via_wypt < dist_to_goal) or \\\n",
        "        (dist_to_goal > self._agent._max_search_steps):\n",
        "      time_step.observation['goal'] = tf.convert_to_tensor(value=waypoint[None])\n",
        "    else:\n",
        "      time_step.observation['goal'] = goal\n",
        "    return self._agent.policy.action(time_step, policy_state, seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fc5_wBqF5QnD"
      },
      "source": [
        "Let's initialize the search policy:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "G5kWC2q4UujP",
        "colab": {}
      },
      "source": [
        "agent.initialize_search(rb_vec, max_search_steps=7)\n",
        "search_policy = SearchPolicy(agent, open_loop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RO6k_DbOzWig"
      },
      "source": [
        "Now, let's plot the search path found by the search policy:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "9OvZr9VIRmYA",
        "outputId": "d38be108-b4db-4430-de2b-702439153d5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        }
      },
      "source": [
        "#@title Search Path. { vertical-output: false, run: \"auto\"}\n",
        "\n",
        "difficulty = 0.6 #@param {min:0, max: 1, step: 0.1, type:\"slider\"}\n",
        "max_goal_dist = eval_tf_env.pyenv.envs[0].gym.max_goal_dist\n",
        "eval_tf_env.pyenv.envs[0].gym.set_sample_goal_args(\n",
        "    prob_constraint=1.0,\n",
        "    min_dist=max(0, max_goal_dist * (difficulty - 0.05)),\n",
        "    max_dist=max_goal_dist * (difficulty + 0.05))\n",
        "ts = eval_tf_env.reset()\n",
        "start = ts.observation['observation'].numpy()[0]\n",
        "goal = ts.observation['goal'].numpy()[0]\n",
        "search_policy.action(ts)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "plot_walls(eval_tf_env.pyenv.envs[0].env.walls)\n",
        "\n",
        "waypoint_vec = [start]\n",
        "for waypoint_index in search_policy._waypoint_vec:\n",
        "  waypoint_vec.append(rb_vec[waypoint_index])\n",
        "waypoint_vec.append(goal)\n",
        "waypoint_vec = np.array(waypoint_vec)\n",
        "\n",
        "plt.scatter([start[0]], [start[1]], marker='+',\n",
        "            color='red', s=200, label='start')\n",
        "plt.scatter([goal[0]], [goal[1]], marker='*',\n",
        "            color='green', s=200, label='goal')\n",
        "plt.plot(waypoint_vec[:, 0], waypoint_vec[:, 1], 'y-s', alpha=0.3, label='waypoint')\n",
        "plt.legend(loc='lower left', bbox_to_anchor=(-0.1, -0.15), ncol=4, fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V78Nfa+N4cdOLbpdIlFZUNYhgcBxsO4JQaG2XWUWp9zDYz4BhGfjTzj8h25ONV25+BUumL0GWnoX/nP6feOXmV5CtZ+NPO/6EpMVWCVFnul6IQGDsMbONUu9jsJ8By7Zwx9g7sGP2DkwbMu2Yx64ecjV23LcDt4+9HaZtnuAZiDKX11sBv38EEomDiES2uV2OkHjw9Az4NB9++Q+/POHjxVnFJ32cKNP5/UNh21HU1v4GipLVZb5/zivz1TDYicgVgcAYAP+nbUrpLKjql1Ncc16Zr4ZdMUTkCkmS4PEMgqL4EIvtgmVF3C5JGAx2InKNJMnw+YZBklTEYjvgOBxw0BsY7ETkKlnW4fMNheOYiMV2w3GOP+WPTheDnYhcpygBeL2VMM0WGAb7178qBjsRuabzvDK2nQDgIBzeDEnS3C6tX+OoGCJyzfFDGh3HRnPzu22ThoU5I+QZYoudiNKGJMkIBsdBkmSEQmth8yS/M8JgJ6K0oig+ZGVVw7LCiEQ2ul1Ov8RgJ6K0o+uFbdMO1CAW2+N2Of0Og52I0pLfPwS6XoJI5FMkk0fcLqdfYbATUdrKyjoPiuJHKLQWlhV3u5x+g8FORGlLljUEg+PhOCbC4XWcw72HONyRiNKaqgaRSBxEQ8PraGx8Cx5PacdjnAWyewx2Ikp7jmMiK2s0EolayLIPmlYAgLNAngi7YoioX9D1CqhqFuLxz9vOUqUTYbATUb8gSTK83sFwHBvJZKPb5aQ1BjsR9Ruy7IGiBGCaTW6XktYY7ETUr6hqHiwrDNs23C4lbfHgKRGlvfZZIAHAshJIJhsQjX6GQGCky5WlJwY7EaW944c0Hj36DmTZj5yci1yqKL2xK4aI+h1dL4FpNsK2eSm97jDYiajf0fWSttExh90uJS0x2Imo31HVPMiyB4ZR63YpaYnBTkT9jiRJ0PViGMZhzh/TDQY7EfVLul4KxzGRTDa4XUraYbATUb+kaYWQJJXdMd1gsBNRvyRJMnR9AAyjFo7juF1OWmGwE1G/peslsO0ETPOo26WkFZ6gRET9VlPTu2hu/htaWj7mPO2dSOnwEaaiosK5++673S4jpXRdx/z581O+3ccffxyGwTk2SAylpR9DUQzIsolEogIzZ94IoHWe9vLye1NSg1vvqWXLltmHDh1SunssLbpibDvzhiu5Fa4MdRJNMumDLFuw7Zgr23frPSVJ0gnzOy2CnYjoTJmmBwCgqrzYdTsGOxH1cyosS4OiMNjbMdiJqN8zTQ8UxYDjmG6XkhY4KoaI+i3DCEDXW6AoSWhaFNHodqhqDnS92O3SXMVgJ6J+q7Gx/UIbDvLydqGgYDqyssa6WlM6YFcMEQlAQjIZgGFwGl+AwU5EgkgmA7DtOEyzxe1SXMdgJyIhGIa/7Ttb7Qx2IhKC42hQ1WxeVQkMdiISiKYVI5k8kvHXQmWwE5EwdH0AACfjL77BYCciYahqHiRJy/juGAY7EQmj9VqoRRl/AJUnKBGRMOrrX0Eksg2JxAGEw1ugKF4AmTc/O4OdiIRhGHXw+4fDtmNQFD88njIArfOzZxJ2xRCRUGRZh6L4YVnNbpfiGgY7EQlHVbNhWWE4TuZdxAdgsBORgGQ5AMdxXLuqktsY7EQkHEXJAgDYdsTlStzBg6dEJAxdL+44UGqaTYjF9sC2jYybn53BTkTC6DyksaXlI1hWFHl5k90ryCXsiiEiIalqLiwrBNvOvMvlMdiJSEiqmgegtUsm0zDYiUhIqpoLADDNoy5XknoMdiISkixrUJQAW+xERCJR1TwGOxGRSFQ1F7Ydh2Vl1olKDHYiElamHkBNi2CX5bQoI6V0Xc+o7RL1te7+t1U1G5Ik9+kBVLfeU85JJsKRHMdJZS3dGjdunLN27Vq3yyBBPfbYY65t+5FHHnFt29SqqWkNJElBTs4lbpfSqyRJWuc4zrjuHuOZp0QkrPr6VxAKfQLTPIJweGPH/aJfeIPBTkTCMow6+HxnIxYDVDUfiuIHIP6FNzKvc5uIMkomzvTIYCciocmyF7KsIZGogeMk3S4nJRjsRCQ8n28IHCeJaHQHHMdyu5w+x2AnIuEpShA+3xBYVhSx2E4A7o8G7Es8eEpEwup84Q2gtVsmFtsDn+9sOI4DSZJcrK7vMNiJSFjdDWmMRnchGv0M0einCARGuVBV32OwE1FG8fuHwHESiMX2QJI88PuHuF1Sr2OwE1HG8ftHwrYTiEY/gyx74PUOdLukXsVgJ6KMI0kSsrLOg20bCIc3QpZ1oS54zVExRJSRJElGMDgOqpqDUGgdkskjbpfUa9hiJ6KMJcsqsrMnYN++x9HY+Bd4vYOhKJ6Ox/vrnDIMdiLKaLLsgarmQZJk2HYLvN6RkOXWcO+vc8qwK4aIMp4s6/D5hsNxbESj22DbCbdL+koY7EREABTFD79/GBzHRDT6KSwr6nZJZ4zBTkTURlGC8PtHAgCi0c9gWWGXKzozDHYiok4UxQe/fyQkSUM8vheJxCG3SzptPHhKRBnv+DllAEBVcwAAodBaOM4YeL1nuVHaGWGwE1HGO9GQRsexEAqtQzi8CbYdh98/PMWVnRl2xRARnYAkKQgGx8PjGYhodAfC4U1wnPSf8pctdiKik5AkCcHgeW1T/u6EbScQDFZDktK3XcxgJyLqgUBgBGTZg5qaZ3H48Ap4vZWQJKXj8XQ6S5XBTkTUQz5fFVQ1G7Ydg2WF4PMNgyzrANLrLNX0/SxBRJSGVDUXPt9w2HYcsdgOt8vpFoOdiOi02XAcG4oScLuQbrErhojoZCZPbv2+ejUAwLYNGMZuKIofHs8g18o6GbbYiYh6yHEsxOP7AAA+39BjDp6mE7bYiYh6KBLZAln2QFF8SCbrj3ksna7AxGAnIuqBeHw/4vH9KCm5A4HACLfLOSkGOxFRN+rrX4Fh1AHVNbA0C/H9iyDLAdi2kfbBzj52IqJuGEYdPJ4K6BENtmZD10uQk3MRksnDbpd2SmyxExEBX45+aVddAyekI9b8KZwE4H/i95AMGQgawLoVXy7XNlomnbDFTkR0IhIgG4C3DlCM/hOXbLETEQFdW94Hl0LyVMD7w/mttxc83vo98QVQfm9KSztd/WcXREREPcIWOxFRNzquqhQ0Wu9om+QrncarnwiDnYioGx1T8LYfKE3z7pfO2BVDRCQYBjsRkWDYFUNEdDJpOE79VNhiJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYiIsEw2ImIBMNgJyISDIOdiEgwDHYSnq7rGbVdItXtAoj62vz5890ugSil2GInIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwahuF0DU1x5//HEYhpHy7eq6jvnz56d8u0RssZPw3Ah1N7dLxGAnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEw2AnIhIMg52ISDAMdiIiwTDYiYgEIzmO43YNkCSpHsA+t+sgMZWUlJwvSVLKGzGO49i1tbWfpHq7lDHOchynqLsH0iLYiYio97ArhohIMAx2IiLBMNiJiATDYCciEgyDnYhIMAx2IiLBMNiJiATDYCciEgyDnYhIMAx2IiLBMNiJiASjnmqB9evXT1NV9RHHcUrAHQERUSrZkiQ1OY6z0TTNBdXV1Tt7stJJJwFbv379NI/H84vKykrD5/PFZVnmjGFERCniOA6SyaTa0tISqK2tdRKJxJzq6uo3T7XeSVvgqqo+UllZaQQCgRhDnYgotSRJgq7rZmFhYXNlZaWpadoPerLeSYPdcZwSn88X750SiYjoTAUCgajjOFU9WfZUfeYyW+pERO6TJAkApJ4sy4OhRESCYbATUdqxbMvtEvo1Bns/J0lS9dy5c8tOd7333nvPN3fu3LK6ujqlL+pyxYQJwzFhwnC3y+hOX77eb7zxRnDu3LllliVGGNaGa5XSJ0vH1IUF+t88hRtuuKGyvLz83N56PgZ7P7dq1apt3/3ud+tPd721a9f6n3766dL6+vpTnstAX11fvt7vvPNO8Omnny61bbu3n9oVK7asyK2P1msrtq7IdbuW/orB3s9deeWVkcGDByfdroNSL5FISKKEeWe/2/S7gs7f6fSlNNhra5fn7t+/qPD4r9ra5SnZM69Zs8YvSVL1W2+9ldV+309/+tMBkiRVz5kzp6M7Y/PmzR5JkqpfeumlnJqaGvVb3/rWWZWVlaN9Pt/5JSUlY77+9a9Xff7551r78suXL8+VJKn6/fff9x2/zQkTJgwfO3bsiPbbkiRV33fffeXz5s0rKS4uHuP1ei8YN27c8Pfee++YdW3bxmOPPTagsrJytKZpFxQVFY25/fbbBx05cuSYv9nxXTFz584tkySpevPmzZ7JkycP8fv955eVlZ37wAMPlLZ/VF+yZEnB/fffXwkA55577mhJkqolSarevn27/lVe30y3adMmz1VXXTU4Pz9/rMfjuaC0tPTc6dOnn/3UU08Vnuz1XrBgQdF55503Iicn57xgMHje2LFjR7z00ks5nZ97+/btuiRJ1QsXLiy65557KgYMGDDG5/Nd8E//9E8Dn3766VIA0HW9uv25U/7L95IjsSPyxrqNWQCwoXZD1pHYEVcan7/61a/yq6qqRnk8nguGDRs28r/+679yJkyYMHxCp66+jRs3eq666qrBwWDwPK/Xe8HYsWNHrFy5Mrvz82zZssVz/fXXV5WXl5/r9XovqKioOPeWW24ZVF9f36fdTCn9GG4YdarHM7BL6zKROKB1t3xvu+SSS6LBYNB6++23g9OmTQsDwOrVq4Ner9des2ZNNoAaAHjzzTeDiqI406ZNC+3fv1/zeDz2o48++kVxcbF54MABbcmSJSUTJ04csXPnzi1+v9+55ZZbmn7wgx8kn3nmmaKLL754f/v2PvnkE+/HH3+ctXjx4r2d6/jv//7vgrKyMuOJJ57YH4/HpZ/+9Kfl11xzzfDt27dvLi4utgBgzpw55c8880zJbbfdVn/dddc1bdmyxbto0aLyrVu3+j766KPtinLy/4sZM2YM+da3vtXwve99r+61117LffLJJ8sGDhxo3H///Y0zZ85s3r1796ElS5aUPv/883sGDRpkAMCgQYPY8v8Kvv71rw/Nzs62nnrqqX1FRUXmgQMH9L/85S85N954Y9O+ffv0E73ee/fu9dxxxx0NZ599dsI0Tem1117LnTVr1hBVVXfOnDmzpfM2nnrqqdIxY8ZEFi9evM+yLOmiiy6KRKNR+eWXXy586623tp3q/yKd1EfqlV1Hdx3TmHhr11vZmqI5STspaYrmLPlwSdG0IdOOeQ2G5A0xigJFfXZA4Q9/+EP2vffeW3XFFVc0LVy48IvDhw+r8+bNG2QYhlRZWZkAgL1792qTJ08eEQgE7EWLFu3Pzc21li5dOuDmm28e+vvf/37nTTfd1AIABw4c0CoqKoyZM2ceKCgoMHfu3Ol56qmnSq+66ir/hg0btvXV73BGwR4KrfUmk0dP+z8oGt3jMc2WLusZRoN65MjbgdN5Lk3Ls4LBcad18pSiKBg/fnxozZo1QQCHLMvCRx99FLz11lvrX3jhhQHNzc1yTgfiSHUAAAyRSURBVE6O/be//S171KhR0by8PDsvLy/xwgsvHGh/DtM0ccUVV4SHDh06ZuXKlTm33357k6ZpuPXWWxt+/etfD2hpafkiOzvbBoBnnnmmMBgMWnfdddeRznUkEglp9erVO9qXu+yyyyKjRo0avWDBguLFixfX1NXVKc8++2zxjBkzGn/729/uB4AbbrihpaioyPzud79b9dJLL+XccsstzSf7XWfPnl17//33NwLA9ddfH3r33XeDL7/8cv7999/fWFZWZg4ePDgBAOPHj4+OHj06cTqvI3V16NAhdf/+/Z7f/e53uzr/be65554jAHCy1/vZZ5/9ov1ny7LwjW98o2X37t2epUuXFh0f7IWFhcm//vWvu2X5y4ZseXl5EgAuv/zyiKalpI3UKx7+fw+XLFu3rERXdEeTtY4+pWgyKrd/f+K9J0qfeO+JUgBI2knZsAzpnup7apdeu/RgX9X14x//uGzw4MHxzq/zeeedF7vsssvOaQ/2BQsWFIdCIXXNmjVb2v+eN910U/OQIUNGP/roo+XtwT59+vTw9OnTw+3PPWXKlPDw4cMTV1999fB3333X97WvfS3WF79DxvWxT548ObRhw4asaDQqvf/++/5QKKQ8/PDDtbquO+1dNO+//35w4sSJofZ1Fi1aVDR8+PCRfr//fE3TqocOHToGALZt2+ZtX2bOnDn1sVhMfu655/IBIBqNSitXriy84YYbGrOyspzjamhuD3UAGD58uDF27NjIxx9/nAUAq1evzkomk9Jtt93W2Hm9u++++4iiKM7q1auDp/o9Z86ceUzwDx8+PFZTUyNGV0v76Jfjvz7+OAsff5x1wsf7UHFxsVlRUZF4+OGHK5588snCzZs3e3q67po1a/yXX375kIKCgrGaplXrul793nvvZe/Zs8d7/LLXXHNNU+dQ789+cc0vDs772ryDEiQnkowo7V+dl+l8vwTJmfe1eQd/cc0v+izUTdPEli1b/Ndee+3Rzq/zpZdeGi0vLzfab7///vvBsWPHhjvvpFVVxYwZM45s27bN395lGo/HpYceeqikqqpqlNfrvUDX9eqrr756OABs3bq1y9+3t5xRi/10W8rtwuH1vu66YlT1gJaff1XkTJ7zdE2dOjX08MMPS6tWrcpat26df/jw4bGBAwea1dXV4XfeeSf77LPPNo4cOaJOmTIlBLT2wf/oRz8a+O1vf7tu+vTpLQUFBaZlWdKVV145Ih6Pd/zlKysrk1OmTGl6/vnni+bOndvwwgsv5DU3NyuzZ8/uMmJlwIAB5vH3FRUVJXfs2OEDgMbGRgUAKioqjnmtNE1Dbm6udfTo0VP+3Y7fhq7rjmEYYiRCGpJlGW+99daOf/3Xfy3793//9/IHHnhALS8vN+67777aefPmnXDU0q5du7Rrrrlm2JAhQ+KLFi3aX1VVZWia5vzoRz8q3717d5c3fmlpqTDdZYqsYOGUhbXThkxrufHlG4eEjJBiWF3/R3VFd7I92ebKG1fumlQ5KdqXNR06dEg1TVMaMGBAl9e5sLCw477m5mZl1KhRXT7plpSUJB3HQUNDg5qfn2/cd9995cuXLx/w/e9//9DEiRPDOTk51r59+/Q77rhjcOf86G0ZN9Rt/PjxsdzcXHPVqlXBTZs2+SdOnNgCAJMmTWp59dVX8wcOHGhomuZMmTIlDAD/8z//k3/xxReHfv3rX3d8XN62bVu3Ld9/+Zd/qb/uuuuGrVmzxv/8888XVVdXh6urq7vsBA8fPtzlda+vr9dKSkqSAFBQUGABwMGDB7Vx477ciSaTSTQ1NSl5eXlddgwZ5aOPtnd7f3ur/ESP97GRI0caf/jDH/bato0PPvjAt3jx4gEPPfTQoKqqqhN2db322ms54XBYWbly5e7Oo5tisVi3b/q208qFcnnl5dEd9+3YMmbpmFEHQwe7vLeK/EXJTfdu2prvy+/zIUClpaWmqqrO4cOHu/RpNTQ0aGVlZQYA5OTkWN0tU1tbq0mShMLCQhMAXnvttfwZM2Y0/uxnPzvUvszrr7/e5wdCUtqC0/ViM5E4oB3/pevFKQsqWZZx0UUXhVavXp29du3a4JVXXhkCWlvyn332mf/111/PPffccyPBYNAGWt9gqqoe05WybNmywu6e+xvf+EaoqqoqPnfu3IHr16/P+va3v91tS2316tU5LS0tHa/99u3b9Y0bNwbGjx8fBoDJkyeHNU1zfv/73+d3Xu+5557LtyxLuvzyy0PHP+fp8ng8DgBEo1G24nuZLMu45JJLYkuXLj0AAJs3b/ad6PVuv63resf/2KZNmzyffPJJFnrI4/HYABAOh/vt39Krep2GaEO3Bwgaog2aT/WlZM4qVVUxevTo6BtvvJHXeSjpmjVr/AcPfrnTueSSS0IbN24MdB5JZpomXn311bxzzjknmp/fuhOKx+Nd8uP555/v82GcKW2xl5Tc2ZTK7Z3IpEmTQvPnzx+kKAqmTp0aAlpHzPj9fuvDDz8Mfu973+vYu15xxRXNS5cuLXnooYdKLrroosiqVauy33jjjbwTPfddd91V/2//9m8Dc3NzzTvuuONod8t4PB5n8uTJw77//e/XxuNxacGCBWVZWVn2D3/4wzoAKC4utv75n/+57plnninx+/32tdde27x161bvwoULyy+44ILwzTfffNIDpz0xZsyYGAD8/Oc/L7rrrrsadV13JkyYEPN6vZz07Qx8+OGHvjlz5gy84YYbjg4bNixuWZa0fPnyAkVRnKlTp4ba39zHv97Tp09veeyxx5xZs2ZVzZ07t+7gwYPawoULy0pKSoyTXSuhs1GjRsUB4Cc/+UnJtdde26yqqnPZZZf1aZdFb3t126vZqqw6CSsheVWvbdmWpEiKE7fisiqrzqvbX82eNXrWV/6/74mHH364ZsaMGUOnTp06+O67726or69XFy5cWFZYWJhsnxRx/vz5dS+//HLB1KlTh82fP78mJyfHWrZsWdG+ffu8K1as6LgYxqRJk5pfeeWVgoULF8aGDRuWWLlyZe66det6vNM+U/12D/9VtIf5qFGjIu17VlVVMWHChBAAtLfiAWDRokU1s2bNqv/Vr35VfMsttwzZunWr780339xxoue+7bbbjgLATTfd1Ojzdd/KuPHGGxunTp3a9MADDwyaPXt2VX5+vvnnP/95e/tQRwBYsmTJwUcfffTAO++8k3PzzTcPWbJkScmMGTMa33777Z29MaTt4osvjs2dO7dm1apVudOmTRsxadKkc/bt29d/hlSkmYqKimR5ebnxy1/+snjWrFlD7rrrrrNra2v1FStW7Lr00kujJ3q9x40bF1+2bNnnBw8e1GfNmjVk8eLFJY888sgXF154YY8/lX3zm99suvXWW+uXL19eNGXKlBGTJk06py9/177w4qYXCyLJiOxVvfbNo25uqH+wfsNNo29q8CpeO5KMyL/bmLqTlf7xH/+xZenSpZ/v2rXLd9tttw3++c9/XrJgwYIDhYWFZjAYtIDWY2qrV6/eNnTo0NiDDz446M477xzc1NSkrlix4pghqs8+++yBK6+8snnBggXld95559nhcFh58cUX9/T173DSKyht3Lhx79ixYxv6ugiRPPnkk4UPPvjgWZs2bdrS3TDCthOUDi1ZsqTGjfqE5nIfO52ZuBmX8hblnadICn5z/W923zDyho5gXPnpyuw7X71zsO3YODrv6AaP6nHlE+Xu3bu1kSNHnjtnzpxD//Ef/3Ho1Gv0jY0bNxaOHTu28lTLZdzB076ybt067/bt2z2LFi0qmzJlShPHhhP1jGmbmHnOzMYFUxbUDMweeMzxtpkjZ7ZcWHHh5h+u+mFZ0k7Cgx6PIj1j4XBY+s53vjPwqquuaikqKjJ37drlWbx4cYnX67Vnz57dLxq6DPZecu+99571ySefBM4///zIs88+u//Ua1CvY0u9X8rSs5wXZ7x4wvfMwOyB5ske722qquLw4cPagw8+OKipqUn1+Xz2uHHjwitWrNh91lln9Yvhpgz2XvJRD0PFcZx1fV0LEZ05r9frvP3227vdruOryMiDp0REImOwExEJ5lTBbtu2Ld6pbkRE/UzbCMYejQo6abBLklQbi8X6bKIaIiLqmUgk4pck6fOeLHvSYDdN87G9e/fqkUjEx5Y7EVFqOY4DwzDUhoaG3L1796rJZPJnPVnvpCcoAcD69eunqar6iOM4JWCfPBFRKtmSJB11HGejaZqPV1dX7zz1Kj0IdiIi6l/YAiciEgyDnYhIMAx2IiLBMNiJiATDYCciEsz/B17B/flLeExJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UIq3YzOeqYkW"
      },
      "source": [
        "Now, we'll use that path to guide the agent towards the goal. On the left, we plot rollouts from the baseline goal-conditioned policy. On the right, we use that same policy to reach each of the waypoints leading to the goal. As before, the slider allows you to change the distance to the goal. Note that only the search policy is able to reach distant goals."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "IK4ukv7TDoWE",
        "colab": {}
      },
      "source": [
        "#@title Rollouts with Search. { vertical-output: true, run: \"auto\"}\n",
        "eval_tf_env.pyenv.envs[0]._duration = 300\n",
        "seed = np.random.randint(0, 1000000)\n",
        "\n",
        "difficulty = 0.8 #@param {min:0, max: 1, step: 0.1, type:\"slider\"}\n",
        "max_goal_dist = eval_tf_env.pyenv.envs[0].gym.max_goal_dist\n",
        "eval_tf_env.pyenv.envs[0].gym.set_sample_goal_args(\n",
        "    prob_constraint=1.0,\n",
        "    min_dist=max(0, max_goal_dist * (difficulty - 0.05)),\n",
        "    max_dist=max_goal_dist * (difficulty + 0.05))\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "for col_index in range(2):\n",
        "  title = 'no search' if col_index == 0 else 'search'\n",
        "  plt.subplot(1, 2, col_index + 1)\n",
        "  plot_walls(eval_tf_env.pyenv.envs[0].env.walls)\n",
        "  use_search = (col_index == 1)\n",
        "  np.random.seed(seed)\n",
        "  ts = eval_tf_env.reset()\n",
        "  goal = ts.observation['goal'].numpy()[0]\n",
        "  start = ts.observation['observation'].numpy()[0]\n",
        "  obs_vec = []\n",
        "  for _ in tqdm.tnrange(eval_tf_env.pyenv.envs[0]._duration,\n",
        "                        desc='rollout %d / 2' % (col_index + 1)):\n",
        "    if ts.is_last():\n",
        "      break\n",
        "    obs_vec.append(ts.observation['observation'].numpy()[0])\n",
        "    if use_search:\n",
        "      action = search_policy.action(ts)\n",
        "    else:\n",
        "      action = agent.policy.action(ts)\n",
        "\n",
        "    ts = eval_tf_env.step(action)\n",
        "  \n",
        "  obs_vec = np.array(obs_vec)\n",
        "\n",
        "  plt.plot(obs_vec[:, 0], obs_vec[:, 1], 'b-o', alpha=0.3)\n",
        "  plt.scatter([obs_vec[0, 0]], [obs_vec[0, 1]], marker='+',\n",
        "              color='red', s=200, label='start')\n",
        "  plt.scatter([obs_vec[-1, 0]], [obs_vec[-1, 1]], marker='+',\n",
        "              color='green', s=200, label='end')\n",
        "  plt.scatter([goal[0]], [goal[1]], marker='*',\n",
        "              color='green', s=200, label='goal')\n",
        "  \n",
        "  plt.title(title, fontsize=24)\n",
        "  if use_search:\n",
        "    waypoint_vec = [start]\n",
        "    for waypoint_index in search_policy._waypoint_vec:\n",
        "      waypoint_vec.append(rb_vec[waypoint_index])\n",
        "    waypoint_vec.append(goal)\n",
        "    waypoint_vec = np.array(waypoint_vec)\n",
        "\n",
        "    plt.plot(waypoint_vec[:, 0], waypoint_vec[:, 1], 'y-s', alpha=0.3, label='waypoint')\n",
        "    plt.legend(loc='lower left', bbox_to_anchor=(-0.8, -0.15), ncol=4, fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2JvMnn7X2mrf"
      },
      "source": [
        "-----------------------------\n",
        "## Next Steps and Open Problems\n",
        "We encourage readers to play around with the experiments. To get started, here are a few questions:\n",
        "1. What effect does distributional RL have on learning distances? (Hint: change `use_distributional_rl` when initializing the `UvfAgent`.)\n",
        "2. What is the effect of using more critic networks in the ensemble? (Hint: change `ensemble_size` when initializing the `UvfAgent`)\n",
        "3. While we applied planning *after* training a goal-conditioned policy, can planning be used to accelerate learning of the goal-conditioned policy? (Hint: Set `UvfAgent.collect_policy` to be the `SearchPolicy`)\n",
        "4. Can you be smart about which observations to include in the replay buffer to make search faster? (Hint: Simple behavior cloning may be enough.)\n",
        "7. Can the search policy be distilled into a single neural network policy?\n",
        "5. What tricks are important for learning distances with RL?\n",
        "6. How can more sophisticated planning algorithms be used in a similar framework?\n",
        "8. Can the same idea by applied to other domains such, as manipulation?\n"
      ]
    }
  ]
}